Research Area 3: Fusion: Tools & Approaches
Project 3.2: Fusion of Spatial-Temporal Sensor Data
Daniel ZengAssociate Professor & Honeywell Fellow
Director, Intelligent Systems & Decisions LabMIS Department
University of Arizona
December 11, 2008 NCBSI Tucson
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Challenges
Modality• Sensors of
different kinds
• Differing granularity, availability, sensitivity, …
Uncertainty• Noises• Ill-
understood causality & correlation
Data Elements• Time• Space• Networks• Patterns• Ill-
understood features
Processing Speed & Resource Utilization• (near)
Real-time requirements
• Resource constraints
Actionable Information• Context-
sensitive• Task-
dependent
• Human in the loop
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Projects in Research Area 3
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Task Team
Project 3.2 Fusion of Spatial-Temporal Sensor Data
Daniel Zeng, Hsinchun Chen; U. of Arizona
Project 3.3 Data Fusion for Decision Support
David Hall, Isaac Brewer; Penn State
Project 3.4 Dynamic Resource Allocation using Market-Based Methods
Tracey Mullen, Isaac Brewer; Penn State
Project 3.5 Reduction of False Alarm Rates from Fused Data
Huan Liu, George Runger, Jeremy Rowe; Arizona State U.
Project 3.2: Fusion of Spatial-Temporal Sensor Data
• Motivation– Analyzing sensor data with prominent
spatial and temporal components and developing related predictive models are of great practical importance to• identify immediate concerns• provide clear situational awareness
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Project 3.2 Technical Objectives
• Develop novel spatial-temporal data analytical techniques to identify and summarize patterns from dynamic and noisy data generated by sensor networks
• Evaluate different formalisms and computational techniques for representing and reasoning about uncertainties in data of different granularity and modality
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Approaches for Spatial-Temporal Sensor Data Integration
• Novel prospective spatial-temporal data clustering techniques– “Hotspot” identification• Markov switching for temporal change detection• SVC-based spatial-temporal change detection
– Exploratory factors and dynamic changes• Theory-based spatial-temporal correlation
measures and inference mechanisms– Integrating “evidence” from multiple data streams
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Major Types of Hotspot Analysis• Retrospective Models: Static Hotspot
Analysis– Given a baseline (data points/events/cases on
a map indicating the normal situation) and new cases of interest, a spatial “Before and After” comparison
– Question: Where??• Prospective Models: Dynamic Hotspot
Analysis– Baseline unknown– Data feed continuously arriving– Question: When and Where??
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Spatial-Temporal Correlation Analysis
To formalize the intuitive notion of correlation “persons residing in or near a dead crow cluster in
the current or prior 1-2 weeks were 2-3 times more likely to become a WNV case than those not residing in or near such clusters” (Johnson et al. 04)
To identify significant correlations among multiple types of events with spatial and temporal components
Representing & Reasoning about Data Uncertainty
• Experimenting with a set of formal methods– Bayesian networks– Granular computing
• Supporting a range of datasets to facilitate data fusion and integrated reasoning – Sensor-generated data– Existing records-based databases
• Fusion architecture– Data fusion? Result fusion?
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Spatial-Temporal Visualizer (STV)
• Providing synchronized, integrated views of spatial temporal data elements– GIS View– Periodic Pattern View– Timeline View
• Hotspot analysis capabilities built-in• SOA Implementation
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Benefits to DHS
• Providing a spatial-temporal data analysis and fusion framework for situational awareness and actionable intelligence• Providing noise-tolerant data representation and evidence-based fusion techniques for data with different resolutions and modalities• Enabling additional operational opportunities when the processed capabilities are in place
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Deliverables and Timelines1Q: Data characterization and analysis contexts2Q: Sensor data spatial-temporal clustering 3Q: Data uncertainty representation4Q: Sensor data clustering with unified uncertainty representationY2: Sensor data correlation analysis and fusion methodsY3: Sensor data uncertainty reasoningY4-6: Sensor data granularity representation and reasoning; evidence-based integrated analytics; evaluation 13
Linkage Among Area 3 Projects
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Linkage with Other BSI ProjectsDrivers Data Fusion Research
Research Area 1: Multi-modal info. fusion for deception/intent detection
Project 3.2: data uncertainty and representationsProject 3.5: change & anomaly detection
Research Area 2: Real-time sensor networks
Project 3.2: spatial-temporal data analysisProject 3.5: change & anomaly detection
Decision-making and -aiding Project 3.3: actionable informationProject 3.4: resource allocation
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Ongoing/Leveraged Research• NSF, “A National Center of Excellence for Infectious Disease Informatics”; “Transnational Public Health Informatics Research” • Multi-source syndromic surveillance, and early warning systems
• CDC’s “BioPHusion” Project• Information fusion & public health situational awareness
• “Smart Carts”– RFID applications in Retailing• Spatial-temporal pattern discovery & path clustering
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Thanks!
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