army high performance computing research center prof. shashi shekhar computational sciences &...
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Army High Performance Computing Research CenterProf. Shashi Shekhar
Computational Sciences
& Engineering for Defense
Technology Applications
Enabling
Technologies
for Scientific
Simulation
Computational
Mechanics
& Simulation
Based Design
High Speed
Flow
Simulations
Materials
Processing
Environmental
Contaminant
Remediation
ComputerScience
ComputationalMechanics
Visualization
FluidsMaterials
Environment
Battlefield
Visualization
for
Training
• High Performance Geographic Information Systems (HPGIS)
• Spatial Databases
• Indexing, Clustering, Storage methods
• Query Processing and Optimization
• Terrain Visualization
Prof. Shashi Shekhar
AHPCRC/Dept. of Computer Science, University of Minnesota
HPGIS
Maps
Battlefield Events Surveillance Data
Battlefield Simulation
Situation Assessment
Soldiers
Research Interests
HPGIS
Situation Assessment
Battlefield Simulation
Maps
Battlefield Events
Surveillance Data
Soldiers
Maps are as important to soldiers as guns
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
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 ?
DisplayGraphics Engine
Local Terrain
Database
Remote Terrain
Databases
Set of Polygons
30 Hz. View Graphics
2Hz.
8Km X 8Km Bounding Box
High Performance GIS
Component
Set of Polygons
25 Km X 25 Km
Bounding Box
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
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
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 VWHERE V.left (F.extent, L.extent) AND L.name = ‘Beech Lake’ AND F.soil-firmness > 5;
Note: Left is a viewer-based “direction” predicate.
BattleField Assesment: A Database Querying Approach S. Shekhar, X. Liu and S.Chawla(U. of M), Dr. J. Gurney, Dr. E. Klipple (ARL Adelphi)
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)
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.
Extension Period
• Open Shape Strategy for Directional Query processing
• Join Index Data Structure
• Spatial Data Mining
• Workshop: Battlefield Visualization and Real Time GIS.
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
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
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
Objective:maximize
classification_accuracy
Constraints:
Spatial Autocorrelation exists
},...{ 1 nssS
RSfkX :
}1,0{: SfY
}1,0{... RR
yf̂
),ˆ( yy ff
Nest locations Distance to open water
Vegetation durability Water depth
SDM Evaluation: Changing Model • Linear Regression
• Spatial Regression
• Spatial model is better
Xy
XWyy
SDM Evaluation: Changing measure
))(.,(),( PnearestAAdistPAADNP kk
k
New measure:
• 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
Accomplishments
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)