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)