Army High Performance Computing Research Center Prof. Shashi Shekhar

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Army High Performance Computing Research
CenterProf. Shashi Shekhar
ComputerScience
Visualization
Enabling Technologies for Scientific Simulation
Battlefield Visualization for Training
ComputationalMechanics
Environment
Computational Sciences Engineering for
Defense Technology Applications
Computational Mechanics Simulation Based
Design
Environmental Contaminant Remediation
High Speed Flow Simulations
Materials Processing
Fluids
Materials
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Prof. Shashi Shekhar AHPCRC/Dept. of Computer
Science, University of Minnesota
Research Interests

• High Performance Geographic Information Systems
  (HPGIS)
• Spatial Databases
• Indexing, Clustering, Storage methods
• Query Processing and Optimization
• Terrain Visualization

Maps
Battlefield Events
Surveillance Data
Soldiers
Situation Assessment
HPGIS
Battlefield Simulation
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Maps are as important to soldiers as guns
Soldiers
Surveillance Data
HPGIS
Battlefield Events
Situation Assessment
Maps
Battlefield Simulation

• Example Usage of Geographic Info. Systems (GIS)
  in Battlefield
• Rescue of pilots after their planes went down
  (recently in Kosovo)
• Precision targeting e.g. avoid accidental bombing
  of friendly embassies
• Logistics of Troop movements, avoid friendly fires

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GIS Analysis by Army

• Tactical (1) Navigate in unfamiliar terrain, (2)
  Avoid friendly fire, (3) Given recent firing
  patterns, locate hidden enemy units.
• Operational (1) Corridor Analysis Identify
  sequence of land parcels suitable for troop
  movement for given unit size and vehicle types ?
  (2) Simulate enemy terrain for training in a
  flight simulator.
• Strategic Which Army Base locations are most
  critical given strategic interests, local
  demographic/political conditions ?

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Parallelizing Range Queries for Battlefield
Simulation

• (1/30) second Response time constraint on Range
  Query
• Parallel processing necessary since best
  sequential computer cannot meet requirement
• Green rectangle a range query, Polygon colors
  shows processor assignment

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Declustering and Load-Balancing Methods to
Parallelize GISS. Shekhar, S. Ravada, V. Kumar
(University of Minnesota), D. Chubb, G. Turner
(US Army)

• Research Objective Meet the response time
  constraint for real time battlefield terrain
  visualization in flight simulator.
• Methodology
• Data-partitioning approach
• Evaluation on Cray T3D, SGI Challenge.
• Results
• Data replication needed for dynamic
  load-balancing, as local processing is cheaper
  than data transfer
• Good de-clustering method needed for dynamic
  load-balancing
• Significance
• A major improvement in capability of geographic
  information systems for determining the subset of
  terrain polygons within the view point (Range
  Query) of a soldier in a flight simulator using
  real geographic terrain data set.

Dividing a Map among 4 processors. Polygons
within a processor have common color
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BattleField Assesment A Database Querying
Approach S. Shekhar, X. Liu and S.Chawla(U. of
M), Dr. J. Gurney, Dr. E. Klipple (ARL Adelphi)

• Research Objective Design of spatial database
  query language for Battlefield decision support
  system.
• Methodology
• Object model for directions. E.g., North,
  Between, Left, 3 O Clock.
• Integrate directional data-types in
  industry-standard query language (SQL) and
  Spatial Library(OGIS).
• Results
• An algebra(value-domain, operators) for
  direction objects.
• Integration of algebra in commercial
  object-relational databases.
• Significance
• A major step towards simple natural language
  like query interface for battlefield decision
  support systems.

Query List the farm fields to the left of the
lake which are suitable for tank movement
? SELECT F.name, F.extent FROM FarmField F,
Lake L,Viewer V WHERE V.left (F.extent,
L.extent) AND L.name Beech Lake AND
F.soil-firmness gt 5 Note Left is a
viewer-based direction predicate.
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Orientation-based Direction Query Processing

• Classical Strategies
• Based on Range query strategy
• Limitations
• May lead to large unnecessary I/O and CPU cost
• Need to know world boundary and calculate the
  intersection of boundary and direction region
• Post Filter step is needed even for MBR objects
• Our approach
• Open shape based strategy(OSS)

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Open Shape based Strategy(OSS)

• Basic idea
• Model direction region as an open shape
• Use actual direction region as a filter
• Advantages
• Improve filtering efficiency by eliminating false
  hits
• Reduce unnecessary I/O and CPU cost
• Eliminate post Filter step for MBR objects
• Do not need to have knowledge of world boundary
• Experimental evaluations
• Consistently outperforms classical range query
  strategy both in I/O and CPU.

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Extension Period

• Open Shape Strategy for Directional Query
  processing
• Join Index Data Structure
• Spatial Data Mining
• Workshop Battlefield Visualization and Real
  Time GIS.

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Spatial Data Mining(SDM)

• Historical Example London Asiatic
  Cholera(Griffith)
• Search of implicit, interesting patterns embedded
  in geo-spatial databases
  
• Reconnaissance
• Vector maps(NIMA, TEC)
• GPS
• Data Mining vs. Statistics High utility local
  trends
• SDM vs. DM Spatial Autocorrelation

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Army Relevance of SDM

• A decision aid in establishing the next service
  center
• location, location, location
• Detection of lost ammunition dumps at civil war
  battlegrounds (Dr. Radhakrishnan)
• Search for local trends in massive simulation
  data stored in Army lab databases
• Army/DoD is one of the biggest landowners.
• pristine environment, home to endangered species
• balance unique defense requirements(training and
  war games) with environmental regulations

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Spatial Data Mining Case Study of location
Prediction

• Given
• 1. Spatial Framework
• 2. Explanatory functions
• 3. A dependent function
• 4. A family of function mappings
• Find A function
• Objectivemaximize
• classification_accuracy
• Constraints
• Spatial Autocorrelation exists

Nest locations
Distance to open water
Water depth
Vegetation durability
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SDM Evaluation Changing Model

• Linear Regression
• Spatial Regression
• Spatial model is better

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SDM Evaluation Changing measure
New measure
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Accomplishments

• Scaleable parallel methods for GIS Querying for
  Battlefield Visualization
• A spatial data model for directions for querying
  battlefield information
• Spatial data mining Predicting Locations Using
  Maps Similarity (PLUMS)
• An efficient indexing method, CCAM, for spatial
  graphs, e.g. Road Maps

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Army Relevance and Collaborations

• Relevance Maps are as important to soldiers as
  guns - unknown
• Joint Projects
• High Performance GIS for Battlefield Simulation
  (ARL Adelphi)
• Spatial Querying for Battlefield Situation
  Assessment (ARL Adelphi)
• Joint Publications
• w/ G. Turner (ARL Adelphi, MD) D. Chubb (CECOM
  IEWD)
• IEEE Computer (December 1996)
• IEEE Transactions on Knowledge and Data Eng.
  (July-Aug. 1998)
• Three conference papers
• Visits, Other Collaborations
• GIS group, Waterways Experimentation Station
  (Army)
• Concept Analysis Agency, Topographic Eng.
  Center, ARL, Adelphi
• Workshop on Battlefield Visualization and Real
  Time GIS (4/2000)