NCOR Wiki - User contributions [en]

Enhanced Object-Based Production Conference

2020-03-24T18:20:26Z

Dgo2112: /* Event Date and Venue */

==Event Date and Venue ==

'''This event is currently postponed due to the coronavirus pandemic.'''

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Conference Goal ==
This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-03-24T18:19:23Z

Dgo2112: /* Event Date and Venue */

==Event Date and Venue ==

This event is currently postponed due to the coronavirus pandemic.

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Conference Goal ==
This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-07T19:22:22Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Conference Goal ==
This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-07T19:22:02Z

Dgo2112: /* Event Date and Venue */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Conference Goal ==
This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-07T19:21:21Z

Dgo2112: /* Conference Purpose */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-06T00:33:47Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The Intelligence Community Ontology Foundry initiative is an effort to create a governing body that would be responsible for curating a collection of upper- and mid-level ontologies used to tag data in the defense and intelligence domains. The goal of this session is to continue work on the ICOF initiative by discussing how it contributes to the semantic foundation for Referent Tracking and Object Based Production.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:08:38Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry'''

9am – 12:00pm

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:07:52Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:07:29Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry</>
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:06:18Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm '''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:06:00Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm ''''Referent Tracking Theory Applied to Object Based Production'''

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:05:34Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm BREAK

'''Part IV'''

3:25pm "Referent Tracking Theory Applied to Object Based Production"

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T15:04:34Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am '''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am '''Using the Common Core Ontologies'''

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am BREAK

10:45am '''Introduction to Defense Ontologies'''

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am '''Object Based Production and Living Intelligence'''

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm '''Recording Reality Using Referent Tracking'''

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm "Referent Tracking Theory Applied to Object Based Production"

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T14:58:33Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''

9:00am Barry Smith, Director, National Center for Ontological Research (NCOR)

:'''Introduction to Basic Formal Ontology (ISO/IEC 21838-2) and Referent Tracking'''

9:40am Ron Rudnicki, Senior Ontologist, CUBRC

:'''Using the Common Core Ontologies'''

10:30am BREAK

10:45am Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

:'''Introduction to Defense Ontologies'''

11:15am Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

:'''Object Based Production and Living Intelligence'''

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T14:28:03Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:40am (40 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:40am – 10:30am (50 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:30am – 10:45am (15 minutes) BREAK

'''Part II'''

10:45 – 11:15 (30 minutes) Introduction to Defense Ontologies

Forrest B. Hare, SAIC Fellow, Solutions Architect, Cyberspace Operation, SAIC

11:15am – 12:00pm (45 minutes) Object Based Production and Living Intelligence

Geoff X. Davis, Program Team Lead, Analytics and Simulation, SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T14:18:38Z

Dgo2112: /* Event Date and Venue */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

==Conference Purpose==

Conference Purpose: This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-02-05T14:17:53Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domains of the defense and intelligence communities, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2020-01-03T17:45:37Z

Dgo2112: /* Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office

1901 Ft. Myer Drive

Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-05T00:10:51Z

Dgo2112: /* Conference Schedule of Topics and Speakers */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-05T00:10:29Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule of Topics and Speakers ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-05T00:08:29Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Wednesday May 13, 2020

'''Day One: Referent Tracking and Object Based Production
'''
'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

'''Day Two: Intelligence Community Ontology Foundry
'''
9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-05T00:07:08Z

Dgo2112:

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Wednesday May 13, 2020

Day One: Referent Tracking and Object Based Production

'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-05T00:06:07Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)

9:45am – 10:45am (60 minutes) Using the Common Core Ontologies

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence

SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T23:43:16Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

9:00am – 9:45am (45 minutes) Introduction to Basic Formal Ontology

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

10:45am – 11:00am (15 minutes) BREAK

'''Part II'''

11:00am – 12:00pm (60 minutes) Object Based Production and Living Intelligence
SAIC

12:00pm – 1:00pm LUNCH

'''Part III'''

1:00pm – 3:10pm (130 minutes) Recording Reality Using Referent Tracking

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

3:10pm – 3:25pm (15 minutes) BREAK

'''Part IV'''

3:25pm – 5:00pm (95 minutes) Referent Tracking Theory Applied to Object Based Production

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

9am – 12:00pm (180 minutes)

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:16:35Z

Dgo2112: /* Presenters */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

'''Part II'''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

'''Part III'''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

'''Part IV'''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:11:33Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

'''Part II'''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

'''Part III'''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

'''Part IV'''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:11:22Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:

[https://davidglimbaugh.com David G. Limbaugh]

dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

'''Part II'''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

'''Part III'''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

'''Part IV'''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:11:04Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:
[https://davidglimbaugh.com David G. Limbaugh]
dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

'''Part II'''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

'''Part III'''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

'''Part IV'''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:10:30Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Day One: Referent Tracking and Object Based Production

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

'''Part II'''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

'''Part III'''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

'''Part IV'''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.

9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:07:51Z

Dgo2112: /* Conference Schedule */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

'''Part I'''

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

''Part II''

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

''Part III''

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

''Part IV''

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo

Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020

Day Two: Intelligence Community Ontology Foundry

Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.
9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:07:04Z

Dgo2112:

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Schedule ==

Part I

Introduction to Basic Formal Ontology: 9:00am – 9:45am (45 minutes)

Barry Smith, Director, National Center for Ontological Research (NCOR)
Using the Common Core Ontologies: 9:45am – 10:45am (60 minutes)

Ron Rudnicki, Senior Ontologist, CUBRC

BREAK: 10:45am – 11:00am (15 minutes)

Part II

Object Based Production and Living Intelligence: 11:00am – 12:00pm (60 minutes)
SAIC

LUNCH – 12:00pm – 1:00pm

Part III

Recording Reality Using Referent Tracking: 1:00pm – 3:10pm (130 minutes)

Werner Ceusters, Division Chief, Biomedical Ontology, Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo

William Hogan, Professor, Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida

BREAK: 3:10pm – 3:25pm (15 minutes)

Part IV

Referent Tracking Theory Applied to Object Based Production: 3:25pm – 5:00pm (95 minutes)

David Limbaugh, Intelligence Community Postdoc, University at Buffalo

Alan Ruttenberg, Director of Clinical and Translational Data Exchange, University at Buffalo Timothy Lebo, Cyber Operations Branch, Air Force Research Laboratory

Nicholas Del Rio, Command and Control Branch, Air Force Research Laboratory

Thursday, May 14, 2020
Day Two: Intelligence Community Ontology Foundry
Purpose: The goal of this session is to continue work on establishing an Intelligence Community Ontology Foundry initiative. Referent Tracking and Object Based Production assume a shared semantic foundation across the intelligence community, which allows for more detailed datasets and a higher potential for knowledge gain.
9am – 12:00pm (180 minutes)

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:03:56Z

Dgo2112: /* Presenters */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

SAIC

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:03:24Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:02:36Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
For information or to register contact:
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-12-04T19:02:05Z

Dgo2112: /* Event Date and Venue */

==Event Date and Venue ==

May 13 – 14, 2020

SAIC Rosslyn Office
1901 Ft. Myer Drive
Arlington, VA 22207

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Intelligence Analysis: A Crash Course

2019-11-08T15:05:20Z

Dgo2112: /* November 18: The Information Ontology */

'''Intelligence Analysis: A Crash Course'''

Special Topic PHI 579

'''Registration''':
:Class#: [http://www.buffalo.edu/class-schedule?switch=showclass&semester=fall&division=GRAD&dept=PHI&regnum=24032 24032] (PHI)

'''Instructors''': [http://ontology.buffalo.edu/smith/shortcv.htm Barry Smith], [http://davidglimbaugh.com/ David Limbaugh]

'''Prerequisites''': Open to all persons with an undergraduate degree.

'''Office hours''': By appointment via email at [mailto:phismith@buffalo.edu phismith@buffalo.edu] or [mailto:dglimbau@buffalo.edu dglimbau@buffalo.edu]

== '''The Course''' ==
''Course Description'': This course will provide an introduction to intelligence analysis, covering both the practical and the theoretical aspects of the intelligence process. We will cover a range of topics including: the nature and goals of intelligence analysis; the cognitive processes involved; the different types of evidence used; analytical reasoning; the role of AI and other forms of computer support to intelligence analysis.

''Course Structure'': This is a three credit hour graduate seminar, with a practical exercise forming part of each class. The final session will be structured around youtube videos created by the students in the class. Students will be trained in the basic tools and methods of intelligence analysis, and will also receive an insight into the overall context into which intellligence analysis fits.

''Target Audience'': The course is open to all interested students, and will presupposen no knowledge of philosophy or of intelligence analysis.

''Intelligence Analysis in Buffalo'': UB scientists are involved in a variety of projects in which intelligence analysis plays a role, and some of the intelligence community collaborators in these projects will be involved in the teaching.

'''Schedule:'''

==August 26: Introduction: Will World War 3 be fought on the internet?==

Will the World War 3 be fought on the internet?
:[https://buffalo.box.com/v/Next-War-On-Internet Slides]
:[https://www.youtube.com/watch?v=KVcIud4I7Ok Video]

An Introduction to the Joint Doctrine Ontology
:[https://buffalo.box.com/s/6jst6ri1hxljzz23gih5bench9ibkozh Slides]
:[https://www.youtube.com/watch?v=rqrVmlHOqC4 Video]

==September 9: Ontology and the Intelligence Process ==

Will feature presentation by [https://www.linkedin.com/in/william-mandrick-ph-d-29b88214/ COL William Mandrick, PhD] on

'''The Intelligence Process'''

[https://buffalo.box.com/v/Joint-Intelligence Slides]

[https://buffalo.box.com/s/7v7dkuo3aw85ykvzno75ej2thshxwa6p Video]

Background reading: [https://www.jcs.mil/Portals/36/Documents/Doctrine/pubs/jp2_0.pdf Joint Intelligence]

==September 16: An Introduction to Basic Formal Ontology==

:[https://buffalo.box.com/v/ICBO-2019-BFO-Tutorial Slides]
:[https://www.youtube.com/watch?v=p0buEjR3t8A Video]

==September 23: Object-Based Production and Referent Tracking==

:Introduction to Object-Based Production
:Agent-Based Intelligence and the philosophy of action

Werner Ceusters, Shahid Manzoor, "[http://www.referent-tracking.com/RTU/files/CeustersICbookRevised/1.0/CeustersICbookRevised.pdf How to track absolutely everything]", ''Ontologies and Semantic Technologies for the Intelligence Community'' (Frontiers in Artificial Intelligence and Applications), 2010, 13-36.

==September 30: Enhanced Object-Based Production==
:RDF triples and Linked Open Data
:Tracking change and tracking errors

:[https://buffalo.box.com/s/im6x8svoq342e6ta6cto4ejzp2ejdmxi Slides]

Frederik Stjernfelt, "[https://buffalo.box.com/s/aasageqt6i8pxw96n417qjxqzd1fj4r2 The ontology of espionage in reality and fiction]", ''Sign Systems Studies'', 31:1 (2003), 133-161

Werner Ceusters and Barry Smith, "[https://philpapers.org/archive/CEUATF.pdf Aboutness: Towards Foundations for the Information Artifact Ontology"], Proceedings of the Sixth International Conference on Biomedical Ontology (ICBO). CEUR vol. 1515. pp. 1-5 (2015).

==October 7: Joint Doctrine Ontology==

:[https://buffalo.box.com/s/6jst6ri1hxljzz23gih5bench9ibkozh Slides]
:[https://www.youtube.com/watch?v=rqrVmlHOqC4 Video]

Peter Morosoff, Ron Rudnicki, Jason Bryant, Robert Farrell, Barry Smith, "[http://ceur-ws.org/Vol-1523/STIDS_2015_T01_Morosoff_etal.pdf Joint Doctrine Ontology: A Benchmark for Military Information Systems Interoperability]", ''Semantic Technology for Intelligence, Defense and Security'' (STIDS), 2015, CEUR vol. 1523, 2-9.

'''Readings''':
:[http://www.jcs.mil/Portals/36/Documents/Doctrine/pubs/dictionary.pdf Department of Defense Dictionary of Military and Associated Terms]
:[http://stids.c4i.gmu.edu/papers/STIDS_2015_T01_Morosoff_etal.pdf P. Morossof et al., "Joint Doctrine Ontology: A Benchmark for Military Information Systems Interoperability" (2015)]

==October 14: Meaningful AI vs. Deep Learning ==

Jobst Landgrebe and Barry Smith, "[https://arxiv.org/pdf/1901.02918.pdf Making AI Meaningful Again]", ''arXiv'', 2019.

:[https://buffalo.box.com/s/s75lli47d3fr7q5pix5a59xg8lb0n6d3 Slides]

Jobst Landgrebe and Barry Smith, "[https://arxiv.org/pdf/1906.05833.pdf Why Turing machines cannot pass the Turing test]", ''arXiv'', 2019.

==October 21: Ontology of Terrorism ==

:[https://buffalo.box.com/v/Ontology-of-Terrorism Slides]
:[https://www.youtube.com/watch?v=G4lg1_-XpiE Video]

:[https://www.researchgate.net/publication/328145335_From_Affective_Science_to_Psychiatric_Disorder_Ontology_as_a_Semantic_Bridge R. R. Larsen and J. Hastings, "From Affective Science to Psychiatric Disorder: Ontology as a Semantic Bridge" (2018)]

==October 28: Ontology of Documents ==

From Speech Acts to Document Acts

:[https://buffalo.box.com/v/Speech-Acts-to-Document-Acts Slides]

IAO-Intel

:[https://buffalo.box.com/v/IAO-Intel-October-28-2019 Slides]

"[http://ontology.buffalo.edu/smith/articles/STIDS-2013.pdf IAO-Intel: An Ontology of Information Artifacts in the Intelligence Domain]”, ''Proceedings of the Eighth International Conference on Semantic Technologies for Intelligence, Defense, and Security'', Fairfax, VA (STIDS 2013), 33-40.

==November 4: Ontology of Normative Entities ==

Deontic Entities in Basic Formal Ontology

:[https://buffalo.box.com/v/Deontic-Entities-in-BFO Slides]

==November 11: Artificial Intelligence, Deep Learning, Machine Learning ==

:Overview of machine learning and other approaches to the exploitation of Big Data
:Role of ontology in Data Science

''Reading''

:[http://arxiv.org/abs/1801.00631 G. Marcus (2018), "Deep Learning: A Critical Appraisal"]

==November 18: The Information Ontology ==

==November 25: Ontology and Data Fusion==

==December 2: Student Projects ==

== '''Further topics''' ==

:Common Knowledge
:Internet of Battlefield Things (IoBT)
:Belief Revision
:Uncertainty
:Geographic Information Science
:Intelligence Preparation of the Operational Environment
:Machine Support for Intelligence Analysis

----
=='''Reading'''==

[https://www.jcs.mil/Portals/36/Documents/Doctrine/pubs/jp2_0.pdf Joint Intelligence] (to be read in advance of COL Mandrick class on September 9)

Robert Arp, Barry Smith and Andrew Spear, [https://mitpress.mit.edu/index.php?q=books/building-ontologies-basic-formal-ontology Building Ontologies with Basic Formal Ontology], Cambridge, MA: MIT Press, August 2015.

Barry Smith, Tatiana Malyuta, David Salmen, William Mandrick, Kesny Parent, Shouvik Bardhan, Jamie Johnson, "[http://ontology.buffalo.edu/smith/articles/Crosstalk-Nov2012.pdf Ontology for the Intelligence Analyst]", ''CrossTalk: The Journal of Defense Software Engineering'', November/December 2012, 18-25.

Terry Janssen, Herbert Basik, Mike Dean, Barry Smith, "[http://ontology.buffalo.edu/smith/articles/Semantic_Reasoning_Framework.pdf A Multi-INT Semantic Reasoning Framework for Intelligence Analysis Support]", in: L. Obrst, T. Janssen, W. Ceusters (eds.), Ontologies and Semantic Technologies for the Intelligence Community (Frontiers in Artificial Intelligence and Applications), Amsterdam: IOS Press, 2010, 57-69.

David Salmen, Tatiana Malyuta, Alan Hansen, Shaun Cronen, Barry Smith, [http://ontology.buffalo.edu/smith/articles/STIDS_2011.pdf Integration of Intelligence Data through Semantic Enhancement]", Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS), George Mason University, Fairfax, VA, November 16-17, 2011, CEUR, Vol. 808, 6-13.

Barry Smith, Tatiana Malyuta, William S. Mandrick, Chia Fu, Kesny Parent, Milan Patel, "http://ontology.buffalo.edu/smith/articles/Horizontal-integration.pdf Horizontal Integration of Warfighter Intelligence Data. A Shared Semantic Resource for the Intelligence Community", Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS), George Mason University, Fairfax, VA, October 23-25, 2012, CEUR 996, 112-119.

----

== '''Student Learning Outcomes''' ==

{| class="wikitable"
|-
! Program Outcomes/Competencies
! Instructional Method(s)
! Assessment Method(s)
|-
| The student will acquire a knowledge of the principles and procedures of intelligence analysis, and an insight into the philosophical methods and theories relevant thereto. The student will also acquire a familiarity with current theoretical research in areas relating to intelligence analysis.
| Lectures and class discussions
| Review of reading matter and associated online content and participation in class discussions
|-
| The student will acquire experience in practical tasks involved in intelligence analysis
| Participation in practical experiments
| Review of results
|-
| The student will acquire experience in communicating the results of work on intelligence analysis and its philosophical understanding
| Creation of youtube presentation and of associated documentation
| Review of results
|}

=='''Important dates'''==
{|
| Sep 20 || - about now start to discuss by email the content of your video and essay with Drs Smith and Limbaugh
|-
| Sep 28 || - submit a proposed title and abstract
|-
| Oct 31 || - submit a table of contents and 300 word summary plus draft of associated ppt slides
|-
| Nov 20 || - submit penultimate draft of essay and powerpoint
|-
| Dec2 || - class presentation
|-
| Dec 4 || - submit final version of essay and powerpoint and upload final version of video to youtube
|}

=='''Grading'''==

Grading will be based on two factors:

I: understanding and criticism of the material presented in classes 1-13

All students are required to take an active part in class (and where relevant on-line) discussions throughout the semester.

II: preparation of a youtube video and associated documentation (including powerpoint slides and essay).

Content and structure of the essay should be discussed with Drs Smith or Limbaugh. Where the essay takes the form of the documentation of a specific ontology developed by the student it should include:
:Statement of scope of the ontology
:Summary of existing ontologies in the relevant domain
:Explanation of how your ontology differs from (or incorporates) these ontologies
:Screenshots of parts of the ontology with some examples of important terms and definitions
:Summaries of potential applications of the ontology

'''Grading Policy:''' Grading follows standard [http://grad.buffalo.edu/Academics/Policies-Procedures/Grading-Procedures.html Graduate School policies]. Grades will be weighted according to the following breakdown:

Weighting Assignment
:40% - class discussions
:20% - youtube video presentation
:20% - powerpoint slides
:20% - essay / ontology content

'''Final Grades'''

Percentages refer to sum of assignment grades as listed above

Grade Quality Percentage
{|
| A || 4.0 || 90.0% -100.00%
|-
| A- || 3.67 || 87.0% - 89.9%
|-
| B+ || 3.33 || 84.0% - 86.9%
|-
| B || 3.00 || 80.0% - 83.9%
|-
| B- || 2.67 || 77.0% - 79.9%
|-
| C+ || 2.33 || 74.0% - 76.9%
|-
| C || 2.00 || 71.0% - 73.9%
|-
| C- || 1.67 || 68.0% - 70.9%
|-
| D+ || 1.33 || 65.0% - 67.9%
|-
| D || 1.00 || 62.0% - 64.9%
|-
| F || 0 || 61.9% or below
|}

An interim grade of Incomplete (I) may be assigned if the student has not completed all requirements for the course. An interim grade of 'I' shall not be assigned to a student who did not attend the course. The default grade accompanying an interim grade of 'I' shall be 'U' and will be displayed on the UB record as 'IU.' The default Unsatisfactory (U) grade shall become the permanent course grade of record if the 'IU' is not changed through formal notice by the instructor upon the student's completion of the course.

Assignment of an interim 'IU' is at the discretion of the instructor. A grade of 'IU' can be assigned only if successful completion of unfulfilled course requirements can result in a final grade better than the default 'U' grade. The student should have a passing average in the requirements already completed. The instructor shall provide the student specification, in writing, of the requirements to be fulfilled.

The university’s Graduate Incomplete Policy can be found [http://grad.buffalo.edu/study/progress/policylibrary.a-to-z.html#iugrade here].

=='''Related Policies and Services'''==

'''Academic integrity''' is a fundamental university value. Through the honest completion of academic work, students sustain the integrity of the university while facilitating the university's imperative for the transmission of knowledge and culture based upon the generation of new and innovative ideas. See http://grad.buffalo.edu/Academics/Policies-Procedures/Academic-Integrity.html.

'''Accessibility resources:''' If you have any disability which requires reasonable accommodations to enable you to participate in this course, please contact the Office of Accessibility Resources in 60 Capen Hall, 645-2608 and also the instructor of this course during the first week of class. The office will provide you with information and review appropriate arrangements for reasonable accommodations, which can be found on the web [http://www.buffalo.edu/studentlife/who-we-are/departments/accessibility.html here].

== '''Background Reading and Video Materials''' ==

*[http://ontology.buffalo.edu/smith/ Streaming video presentations and training courses in ontology]

*[http://www.sciencedirect.com/science/article/pii/S1877050913000690 Concept Analysis to Enrich Manufacturing Service Capability Models]

*[http://www.sciencedirect.com/science/article/pii/S0166361514000438 Supply Chain Management Ontology]

*[http://link.springer.com/article/10.1007/s40436-014-0073-2 Ontology-based interoperability solutions for textile supply chain]

*[http://ontology.buffalo.edu/smith/articles/ontologies.htm Ontology: An Introduction]

*[http://ontology.buffalo.edu/smith/articles/Horizontal-integration.pdf Horizontal Integration of Warfighter Intelligence Data]

*[http://ncorwiki.buffalo.edu/index.php/Ontology_for_Intelligence,_Defense_and_Security Ontology for Intelligence, Defense and Security (2012)]

*[http://militaryontology.org Military Ontology]

Enhanced Object-Based Production Conference

2019-08-24T14:29:23Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-24T14:22:12Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''The Research:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that has the potential to rapidly handle the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T23:05:09Z

Dgo2112: /* Presenters */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G. Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T23:04:48Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G. Limbaugh] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T23:04:31Z

Dgo2112: /* Organizer and Contact */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T23:04:21Z

Dgo2112: /* Presenters */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

William S. Mandrick (SAIC)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T23:02:56Z

Dgo2112: /* Presenters */

Enhanced Object-Based Production Conference

2019-08-21T00:14:52Z

Dgo2112: /* Conference Description */

Enhanced Object-Based Production Conference

2019-08-21T00:14:00Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the most advanced work in medicine to address this problem using the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T00:13:27Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

""A Solution:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the most advanced work in medicine to address this problem using the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T00:12:36Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''Enhanced Object-Based Production:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the most advanced work in medicine to address this problem using the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T00:08:08Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''Enhanced Object-Based Production:''' Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the most advanced work in medicine to address this problem using the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

== Participants ==

TBA

Enhanced Object-Based Production Conference

2019-08-21T00:07:15Z

Dgo2112: /* Enhanced Object-Based Production */

Enhanced Object-Based Production Conference

2019-08-21T00:06:31Z

Dgo2112: /* Conference Description */

==Event Date and Venue ==

''More information forthcoming''

Venue: Washington, DC

Date: May, 2020

== Organizer and Contact==
[https://davidglimbaugh.com David G Limbaugh, PhD] dglimbau@buffalo.edu

== Conference Description ==
The goal of this conference is to enhance Object-Based Production (OBP) by drawing on a combination of [http://www.referent-tracking.com/RTU/reftrackparadigm.html Referent Tracking] and semantic technology.

'''The Problem:''' Ever increasing quantities of disaggregated data pose a problem for intelligence analysis. The problem is magnified when much of the data is sparse, obscure, or ever-changing. A key contributor to this problem is the inconsistency of data management policies. Mutually incompatible data management solutions have been and continue to be adopted not only by organizations but also by different departments within organizations. Data, as a result, is difficult to aggregate, and difficult to discover and to interpret, sometimes even difficult to interpret by those who created the data in the first place. This problem of too-much data inconsistently handled has an analogue in medicine in the realm of patient data. Electronic Health Records, for example, are collections of data about patients which grow and change in ways which make it difficult to track the medical state of a patient as it changes over time, for example as patients move between hospitals.

'''Conference Purpose:''' This conference aims (1) to identify the lessons learned from the most advanced work in medicine to address this problem using the Referent Tracking methodology, (2) to explore how these lessons might be translated to the domain of intelligence analysis, (3) to identify potential benefits relating to semantic technology, and (4) to explore how these benefits can enhance OBP.

== Enhanced Object-Based Production ==
Intelligence data is useful only if it is available to decision makers when they need it. The subject of this conference is an Intelligence Community (IC) inter-organizational data architecture that will enable rapid handling of the enormous amounts of data collected continuously by the IC. The data architecture –– called ‘Enhanced Object-Based Production’ (E-OBP) –– is based on the Referent Tracking (RT) approach developed and tested in the medical domain over some 15 years [http://www.referent-tracking.com/RTU/papers.html]. E-OBP takes the object-oriented approach of Object-Based Production (OBP) but expands ‘object’ to any salient portion of reality. This allows traditional OBP to be transformed into an expressive, flexible, and scalable, data architecture.

The governing principle of E-OBP is to structure data that objectively mirrors reality in a way that allows tracking. Reality is made of unique entities with shared features and relationships indexed to locations and times. E-OBP uses 1) unique identifiers to refer to unique entities, 2) terms from a controlled vocabulary to represent features, relationships, times, and places, and 3) time-indexed, first-order logic expressible, assertions to represent when an entity has some feature or some relationship to other entities.

E-OBP applies not only to data about first-order reality – tanks, people, missions, economic transactions, and so on – but also to data about these data, which it tracks using the same information infrastructure. It tracks when data become available, who made it available, the methods by which it was obtained, and whether it should be trusted [https://buffalo.box.com/s/jryzqr7nh85eu41k0skz1jk8xex9fne8]. It also records when data is discovered to be inaccurate, in a way that allows for more sophisticated error tracking. All of these data are brought together by the system to form a gigantic evolving graph, which forms a comprehensive and continuously adjusted picture of reality structured to allow zooming on identified threats, sensitive areas, government actions, and so forth.

== Presenters ==
[https://scholar.google.com/citations?hl=en&user=icGNWj4AAAAJ Barry Smith] (University at Buffalo)

[https://davidglimbaugh.com David G Limbaugh] (University at Buffalo)

[https://scholar.google.com/citations?user=JLM7L2EAAAAJ&hl=en Ron Rudnicki] (CUBRC, Inc.)

Timothy Lebo (Air Force Research Laboratory)

[http://www.referent-tracking.com/RTU/ceusters_vita.html Werner Ceusters] (University at Buffalo)

[https://hobi.med.ufl.edu/about/faculty-directory-2/hogan-bill/ William Hogan] (University of Florida)

== Participants ==

TBA