research area 3: fusion: tools & approaches project 3.2: fusion of spatial-temporal sensor data

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!