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Integrating Multi-Media with Geographical Information in the
BORG Architecture
R. George
Department of Computer Science
Clark Atlanta University
Atlanta, GA
Outline
• The BORG Architecture– Where We Fit In
• Spatio-Temporal Query Models• Applications
– The Weather Information and Tactical Support (WITS) System
– Integrating Multimedia with GIS
• Conclusion
Battlefield Organic Robotic Grid
• Ubiquitous Knowledge Environment for the Battlefield
• Built upon an ad-hoc computing environment configured as a single computing resource
BORG Experimental Architecture & Platform
IMPACT / PASTA / P2P
Java/C++ Sequal Server/Oracle UML Cognitive Models Sun JXTA
B3A
Imagery DB
Soldier
HighlightsJCDBGIS...Requirements,
CognitiveDeficiencies
Autonomous Fusionand NavigationDistinct Sources
Data Streams
Content Based MessagingSystem
RSSRSSRSS...
Advanced Interactions
Story
Visualizations
Commander/Analysts
Tactical OperationsCenter Workstations
Command and Control
Interactive Fusion andNavigationSummary of Source
Website
Plans
WWWHeterogeneousData sources
KnowledgeEngineering,
Cognitive Models,HCI
Data Related Issues
• Heterogeneous Data Sources
• Multiple Data Types
• Flexible Querying with Levels of Confidence
• Need for Semantic Queries
• Performance
Research Objectives
• Spatial, Temporal Dimensions Features of Data– Characterization of Spatio-Temporal Query Types
• Applications: – Support for Operational Planning
• Long term weather patterns• Detect weather anomalies not predicted by forecast models• Integration with External Knowledge Bases
– Situational Awareness in an Urban Environment• Integration of Heterogeneous Databases including Multi-Media
• Approach: Fuzzy Logic provides a expressive query mechanism for the spatial and temporal domains
Spatial Properties
• Minimum Bounding Rectangle basis for Query– The location of an object Ai is defined by the rectangular area (a
region) [(Xi, Yi), (Xj, Yj)] where Xi Xj, Yi Yj.
– The spatial property of an object (of interest) A is a tuple (R, I), where, R is a rectangular area (a region) which covers a minimal area in which object A appears during the time interval I = [ti, tf]. R is an approximation of a MBR.
– A static or moving in the rectangle R during the interval I. • When A is spatially static, R is the minimum bounding rectangle of A.
Relationships between Objects
• Defined using the Spatial Relationship between MBRs.
• Extension of Allens Temporal Interval Algebra [Li et al]
Relationships between Objects
Relation DefinitionA BOTTOM B Ax {b, bi, m, mi, o, oi, d, di, s, si, f, fi, e} Bx Ay {b, m} By
A TOP B Ax {b, bi, m, mi, o, oi, d, di, s, si, f, fi, e} Bx Ay {bi, mi} By
A LEFT B Ax {b, m} Bx Ay { b, bi, m, mi, o, oi, d, di, s, si, f, fi, e} By
A RIGHT B Ax {bi, mi} Bx Ay { b, bi, m, mi, o, oi, d, di, s, si, f, fi, e} By
A TOP-LEFT B (Ax {b,m} Bx Ay {bi, mi, oi} By ) (Ax {o} Bx Ay {bi,mi} By)
A TOP-RIGHT B (Ax {bi, mi} Bx Ay {bi, mi, oi} By ) (Ax {oi} Bx Ay { bi,mi }By)
A BOTTOM-LEFT B (Ax {b,m} Bx Ay {b,m,o} By ) (Ax {o} Bx Ay {b,m} By)
A BOTTOM-RIGHT B (Ax {b,m} Bx Ay {b,m,o} By ) (Ax {oi} Bx Ay {b,m} By)
A OVERLAPS B Ax {d, di, s, si, f, fi, o, oi, e} Bx Ay { d, di, s, si, f, fi, o, oi, e } By
A EQUAL B Ax {e} Bx Ay {e} By
A INSIDE B Ax {d} Bx Ay {d} By
A CONTAIN B Ax {di} Bx Ay {di} By
A TOUCH B (Ax {m, mi} Bx Ay {d, di, s, si, f, fi, o, oi, m, mi, e} By) (Ax {d, di, s, si, f, fi, o, oi, m, mi, e} Bx Ay {m, mi } By)
A DISJOINT B Ax {b, bi} Bx Ay {b, bi} By
Relationship between Objects
• The fuzzy spatio-temporal relationship during time interval, I is Ai (, , I) Aj is a relation; is the value of membership
and Ai ( )Aj is true during the interval I.– Ex: takes values of WEST, NORTH,
NORTH-WEST
Query Model
• Query is performed on regions within the underlying data structure that form a Minimum Bounding Rectangle.
• Query Model supports queries in the spatial and temporal dimensions – Spatial Search: S, retrieves all regions, Rj, whose area is equal to that of
the user selected MBR, Ri, and field values are similar to those of Rj at Time, Tk
S(MBR) : {Rj | Equal(Ri, R) Λ Disjoint(Ri, Rj) Λ (, ≥ ε)}Tk
– Temporal search :, retrieves region, R, within a time interval, (T0, Tn), to retrieve all instances in which the attribute values are similar. The spatial domain is constant in this operation.
(MBR) = {Tj, R | Equal(Ri, R) Λ (, ≥ ε)} T0-Tn
– Spatio-Temporal Search: ST, identifies similar regions in space and time. The MBRs adjacent to the original are examined to track the weather in the spatial domain. The coordinates of the adjacent MBRs are computed as in TableST(MDC) = {Tj, Rj | Similar(Ri, R) Λ Adj(Ri, Rj) Λ (, ≥ ε)} T0-Tn
Application: The Weather Information and Tactical Support System (WITS)
• Objective: Development of a Weather Data Repository for Operational Planning– Need to know long term weather conditions– Detect weather anomalies not predicted by forecast models– Integration with External Knowledge Bases
• Development of an OLAP Weather Repository– Sources: Georgia Weather (1981-2002)
• US National Weather Service, Georgia Environmental Network, ASOS• Modular Development of WITS
– Ad-hoc Querying (IQ)– Real time Analysis and Planning (TAPS)
• Effects on Operational Systems• Integration with External Knowledge Bases
– Data Mining (DM)
WITS System DesignUSER
INTERFACE
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DATA WAREHOUSE
DATAMINING
MODULES
QUERYMODULES
KNOWLEDGEBASES
(IWEDA)
DATA CLEANING& TRANSFORMATION
DATAACQUISITION AGENTS
REAL TIME MODULE
TAPS MODULE
IQ MODULE
WITS: Information Query (IQ)
• Module for Spatio-Temporal Ad-Hoc Querying – GUI Driven
– Drill Down and Roll Up Capabilities
WITS/IQ: Drill Down
WITS: Tactical Analysis and Planning (TAPS)
• Integration with External Knowledge Bases– Understand Weather
Effects on Systems
– Logistical/Route Planning
WITS: Data Mining (DM)
• OLAP and Data Mining Module to show trends and artifacts in the data
• Detect local weather anomalies not predicted by weather forecasts
• Example: Trend Analysis of Winter weather in Georgia
WITS/DM
Application: Situational Awareness in an Urban Environment
• Heterogeneous Data Sources– Regional Planning, Census, Crime Statistics– Traffic Cams, Overflights– Weather Data (Real-Time, Historical)– Geographical Information
• Maps with Attributes
• Challenges– Multiple Formats, Scales– Co-ordinates – Missing Data
Conflation
• Conflation: integration of data from various data sources into digital maps
• Multiple steps– Feature Matching– Positional Re-alignment – Attribute Deconfliction
• Query Support
Web services
WEBSERVER
MAP WEBSERVICE
VIDEO DATASERVICE
DATASERVICES
AGENTS
Agent-Based Conflation
VideoGIS System
VideoGIS System: Crime Data
VideoGIS System: Video Tracking 1
VideoGIS System: Video Tracking 2
VideoGIS System: Query Results
Conclusions/Future Work
• Integration of Spatio-Temporal Data, with Multi-Media is challenging
• Several areas of theoretical development of in – Basic OLAP operations (approximations rollup, drill
down)
– Query Models
• Practical Applications in Earth Science and Scientific Data Management