
Activity-Based Intelligence
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Content
- 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
- Index
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