Activity-Based Intelligence

Name: Activity-Based Intelligence | Principles and Applications
Brand: Artech House
Price: 159.99 EUR
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Principles and Applications

Patrick Biltgen(Author)

Artech House (Publisher)

1st Edition

Published on 31. December 2015

484 pages

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978-1-60807-877-6 (ISBN)

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Activity-Based Intelligence: Principles and Applications
Foreword
Preface
1 Introduction and Motivation
1.1 The Fourth Age of Intelligence
1.1.1 An Era of Dynamic Change and Diverse Threats
1.1.2 The Convergence of Technology and the Dawn of Big Data
1.1.3 Multi-INT Tradecraft: Visualization, Statistics, and Spatiotemporal Analysis
1.1.4 The Need for a New Methodology
1.2 Introducing ABI
1.2.1 The Primacy of Location
1.2.2 From Target-Based to Activity-Based
1.2.3 Shifting the Focus to Discovery
1.2.4 Discovery Versus Search
1.2.5 Discovery: An Example
1.2.6 Summary: The Key Attributes of ABI
1.3 Organization of this Textbook
1.4 Disclaimer About Sources and Methods
1.5 A Focus on Geospatial Intelligence (GEOINT)
1.6 Suggested Readings
References
2 ABI History and Origins
2.1 Wartime Beginnings
2.2 OUSD(I) Studies and the Origin of the Term ABI
2.3 Human Domain Analytics
2.4 ABI Research and Development
2.5 ABI-Enabling Technology Accelerates
2.6 Evolution of the Terminology
2.7 Summary
References
3 Discovering the Pillars of ABI
3.1 The First Day of a Different War
3.2 Georeference to Discover: "Everything Happens Somewhere"
3.2.1 First-Degree Direct Georeference
3.2.2 First-Degree Indirect Georeference
3.2.3 Second-Degree Georeference
3.3 Discover to Georeference Versus Georeference to Discover
3.4 Data Neutrality: Seeding the Multi-INT Spatial Data Environment
3.5 Integration Before Exploitation: From Correlation to Discovery
3.6 Sequence Neutrality: Temporal Implications for Data Correlation
3.6.1 Sequence Neutrality's Focus on Metadata: Section 215 and the Bulk Telephony Metadata Program Under the USA Patriot Act
3.7 After Next: From Pillars, to Concepts, to Practical Applications
3.8 Summary
References
4 The Lexicon of ABI
4.1 Ontology for ABI
4.2 Activity Data: "Things People Do"
4.2.1 "Activity" Versus "Activities"
4.2.2 Events and Transactions
4.2.3 Transactions: Temporal Registration
4.2.4 Event or Transaction? The Answer is (Sometimes) Yes
4.3 Contextual Data: Providing the Backdrop to Understand Activity
4.4 Biographical Data: Attributes of Entities
4.5 Relational Data: Networks of Entities
4.6 Analytical and Technological Implications
4.7 Summary
References
5 Analytical Methods and ABI
5.1 Revisiting the Modern Intelligence Framework
5.2 The Case for Discovery
5.3 The Spectrum of "INTS" and Exploitation Versus Finished Intelligence
5.4 Decomposing an Intelligence Problem for ABI
5.5 The W3 Approaches: Locations Connected Through People and People Connected Through Locations
5.5.1 Relating Entities Through Common Locations
5.5.2 Relating Locations Through Common Entities
5.6 Assessments: What Is Known Versus What Is Believed
5.7 Facts: What Is Known
5.8 Assessments: What Is Believed or "Thought"
5.9 Gaps: What Is Unknown
5.10 Unfinished Threads
5.11 Leaving Room for Art And Intuition
References
6 Disambiguation and Entity Resolution
6.1 A World of Proxies
6.2 Disambiguation
6.3 Unique Identifiers-"Better" Proxies
6.4 Resolving the Entity
6.5 Two Basic Types of Entity Resolution
6.5.1 Proxy-to-Proxy Resolution
6.5.2 Proxy-to-Entity Resolution: Indexing
6.6 Iterative Resolution and Limitations on Entity Resolution
References
7 Discreteness and Durability in the Analytical Process
7.1 Real World Limits of Disambiguation and Entity Resolution
7.2 Applying Discreteness to Space-Time
7.3 A Spectrum for Describing Locational Discreteness
7.4 Discreteness and Temporal Sensitivity
7.5 Durability of Proxy-Entity Associations
7.7 Summary
References
8 Patterns of Life and Activity Patterns
8.1 Entities and Patterns of Life
8.2 Pattern-of-Life Elements
8.3 The Importance of Activity Patterns
8.4 Normalcy and Intelligence
8.5 Representing Patterns of Life While Resolving Entities
8.5.1 Graph Representation
8.5.2 Quantitative and Temporal Representation
8.6 Enabling Action Through Patterns of Life
References
9 Incidental Collection
9.1 A Legacy of Targets
9.2 Bonus Collection from Known Targets
9.3 Defining Incidental Collection
9.4 Dumpster Diving and Spatial Archive and Retrieval
9.5 Rethinking the Balance Between Tasking and Exploitation
9.6 Collecting to Maximize Incidental Gain
9.7 Incidental Collection and Privacy
9.8 Summary
References
10 Data, Big Data, and Datafication
10.1 Data
10.1.1 Classifying Data: Structured, Unstructured, and Semistructured
10.1.2 Metadata
10.1.3 Taxonomies, Ontologies, and Folksonomies
10.2 Big Data
10.2.1 Volume, Velocity, and Variety.
10.2.2 Big Data Architecture
10.2.3 Big Data in the Intelligence Community
10.3 The Datafication of Intelligence
10.3.1 Collecting It "All"
10.3.2 Object-Based Production (OBP)
10.3.3 Relationship Between OBP and ABI
10.4 The Future of Data and Big Data
10.5 Summary
References
11 Collection
11.1 Introduction to Collection
11.2 MOVINT with Motion Imagery
11.2.1 FMV
11.2.2 WAMI
11.3 MOVINT from Radar
11.3.1 Basic Principles of GMTI
11.3.2 Evolution of GMTI Collection Systems
11.4 Additional Sources of Activities and Transactions
11.5 Collection to Enable ABI
11.6 Persistence: The All-Seeing Eye (?)
11.7 The Persistence "Master Equation"
11.8 Space-Based Persistent Surveillance
11.8.1 Space-Based GMTI
11.8.2 Commercial Space Radar Applications
11.8.3 Space-Based Persistent EO Imagery
11.9 Summary
References
12 Automated Activity Extraction
12.1 The Need for Automation
12.2 Data Conditioning
12.3 Georeferenced Entity and Activity Extraction
12.4 Object and Activity Extraction from Still Imagery
12.5 Object and Activity Extraction from Motion Imagery
12.5.1 Activity Extraction from Video
12.5.1 Activity and Event Extraction from WAMI
12.6 Tracking and Track Extraction
12.6.1 The Role of Sampling Rate and Resolution
12.6.2 Terminology: Tracks and Tracklets
12.6.3 The Kalman Filter
12.6.4 Probabilistic Tracking Frameworks
12.6.5 Clustering, Track Association, and Multihypothesis Tracking (MHT)
12.6.6 Detecting Anomalous Tracks
12.7 Metrics for Automated Algorithms
12.8 The Need for Multiple, Complimentary Sources
12.9 Summary
12.10 Acknowledgments
References
13 Analysis and Visualization
13.1 Introduction to Analysis and Visualization
13.1.1 The Sexiest Job of the 21st Century.
13.1.2 Asking Questions and Getting Answers
13.2 Statistical Visualization
13.2.1 Scatterplots
13.2.2 Pareto Charts
13.2.3 Factor Profiling
13.3 Visual Analytics
13.4 Spatial Statistics and Visualization
13.4.1 Spatial Data Aggregation
13.4.2 Tree Maps
13.4.3 Three-Dimensional Scatterplot Matrix
13.4.4 Spatial Storytelling
13.5 The Way Ahead
References
14 Correlation and Fusion
14.1 Correlation
14.1.1 Correlation Versus Causality
14.2 Fusion
14.2.1 A Taxonomy for Fusion Techniques
14.2.2 Architectures for Data Fusion
14.2.3 Upstream Versus Downstream Fusion
14.3 Mathematical Correlation and Fusion Techniques
14.3.1 Bayesian Probability and Application of Bayes's Theorem
14.3.2 Dempster-Shafer Theory
14.3.3 Belief Networks
14.4 Multi-INT Fusion For ABI
14.5 Examples of Multi-INT Fusion Programs
14.5.1 Example: A Multi-INT Fusion Architecture
14.5.2 Example: The DARPA Insight Program
14.6 Summary
References
15 Knowledge Management
15.1 The Need for Knowledge Management
15.1.1 Types of Knowledge
15.2 Discovery of What We Know
15.2.1 Recommendation Engines
15.2.2 Data Finds Data
15.2.3 Queries as Data
15.3 The Semantic Web
15.3.1 XML
15.3.2 Resource Description Framework (RDF)
15.4 Graphs for Knowledge and Discovery
15.4.1 Graphs and Linked Data
15.4.2 Provenance
15.4.3 Using Graphs for Multianalyst Collaboration
15.5 Information and Knowledge Sharing
15.6 Wikis, Blogs, Chat, and Sharing
15.7 Crowdsourcing
15.8 Summary
References
16 Anticipatory Intelligence
16.1 Introduction to Anticipatory Intelligence
16.1.1 Prediction, Forecasting, and Anticipation
16.2 Modeling for Anticipatory Intelligence
16.2.1 Models and Modeling
16.2.2 Descriptive Versus Anticipatory/Predictive Models
16.3 Machine Learning, Data Mining, and Statistical Models
16.3.1 Rule-Based Learning
16.3.2 Case-Based Learning
16.3.3 Unsupervised Learning
16.3.4 Sensemaking
16.4 Rule Sets and Event-Driven Architectures
16.4.1 Event Processing Engines
16.4.2 Simple Event Processing: Geofencing, Watchboxes, and Tripwires
16.4.3 CEP
16.4.4 Tipping and Cueing
16.5 Exploratory Models
16.5.1 Basic Exploratory Modeling Techniques
16.5.2 Advanced Exploratory Modeling Techniques
16.5.3 ABM
16.5.4 System Dynamics Model
16.6 Model Aggregation
16.7 The Wisdom of Crowds
16.8 Shortcomings of Model-Based Anticipatory Analytics
16.9 Modeling in ABI
16.10 Summary
References
17 ABI in Policing
17.1 The Future of Policing
17.2 Intelligence-Led Policing: An Introduction
17.2.1 Statistical Analysis and CompStat
17.2.2 Routine Activities Theory
17.3 Crime Mapping
17.3.1 Standardized Reporting Enables Crime Mapping
17.3.2 Spatial and Temporal Analysis of Patterns
17.4 Unraveling the Network
17.5 Predictive Policing
17.6 Summary
17.7 Further Reading
17.8 Chapter Author Biography
References
18 ABI and the D.C. Beltway Sniper
18.1 Introduction
18.2 Georeference to Discover
18.3 Integration Before Exploitation
18.4 Sequence Neutrality
18.5 Data Neutrality
18.6 Summary
18.7 Chapter Author Biography
References
19 Analyzing Transactions in a Network
19.1 Analyzing Transactions with Graph Analytics
19.2 Discerning the Anomalous
19.3 Becoming Familiar with the Data Set
19.4 Analyzing Activity Patterns
19.4.1 Method: Location Classification
19.4.2 Method: Average Time Distance
19.4.3 Method: Activity Volume
19.4.4 Activity Tracing
19.5 Analyzing High-Priority Locations with a Graph
19.6 Validation
19.7 Summary
19.8 Chapter Author Biography
References
20 ABI and the Search for Malaysian Airlines Flight 370
20.1 Introduction
10.2 Data Sparsity, Suppositions, and Misdirections
20.3 The Next Days: Fixating on the Wrong Entity
20.4 Wide Area Search and Commercial Satellite Imagery
20.4.1 A Tradecraft Breakthrough: Crowdsourced Imagery Exploitation
20.4.2 Lessons Learned in Crowdsourced Imagery Search
20.5 A Breakthrough: Sequence and Data Neutral Analysis of Incidentally Collected Data
20.6 Summary: The Search Continues
20.7 Chapter Author Biography
References
21 Visual Analytics for Pattern-of-Life Analysis
21.1 Applying Visual Analytics to Pattern-of-Life Analysis
21.1.1 Overview of the Data Set
21.1.2 Exploring the Activities and Transactions of Two Randomly Selected Users
21.1.3 Identification of Cotravelers/Pairs in Social Network Data
21.2 Discovering Paired Entities in a Large Data Set
21.3 Summary
21.4 Acknowledgements
References
22 Multi-INT Spatiotemporal Analysis
22.1 Overview
21.2 Human Interface Basics
22.2.1 Map View
22.2.2 Timeline View
22.2.3 Relational View
22.3 Analytic Concepts of Operations
22.3.1 Discovery and Filtering
22.3.2 Forensic Backtracking
22.3.3 Watchboxes and Alerts
22.3.4 Track Linking
22.4 Advanced Analytics
22.5 Information Sharing and Data Export
22.6 Summary
References
23 Pattern Analysis of Ubiquitous Sensors
23.1 Entity Resolution Through Activity Patterns
23.2 Temporal Pattern of Life
23.3 Integrating Multiple Data Sources from Ubiquitous Sensors
23.4 Summary
References
24 ABI Now and Into the Future
24.1 An Era of Increasing Change
24.2 ABI and a Revolution in Geospatial Intelligence
24.3 ABI and Object-Based Production
24.4 ABI Applied to Overhead Reconnaissance
24.5 The Future of ABI in the Intelligence Community
24.6 Conclusion
24.7 Chapter Author Biography
References
25 Conclusion
About the Authors
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