Army High Performance Computing Research Center Prof Shashi

Army High Performance Computing Research Center Prof. Shashi Shekhar Computer Science Enabling Technologies for Scientific Simulation Visualization Battlefield Visualization for Training Computational Mechanics Environmental Contaminant Remediation Computational Sciences & Engineering for Defense Technology Applications Materials Processing Materials High Speed Flow Simulations Fluids Computational Mechanics & Simulation Based Design

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 Maps • Terrain Visualization Battlefield Events Surveillance Data Soldiers Situation Assessment HPGIS Battlefield Simulation

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

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 ?

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 Set of Polygons Graphics Engine Display 2 Hz. Set of Polygons Local Terrain Database 25 Km X 25 Km 8 Km X 8 Km Bounding Box 30 Hz. View Graphics Bounding Box High Performance GIS Component Remote Terrain Databases

Declustering and Load-Balancing Methods to Parallelize GIS S. 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 T 3 D, 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 loadbalancing 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

Battle. Field 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 Farm. Field F, Lake L, Viewer V WHERE V. left (F. extent, L. extent) AND L. name = ‘Beech Lake’ AND F. soil-firmness > 5; Note: Left is a viewer-based “direction” predicate.

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 • 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/Do. D 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 Nest locations Distance to open water Objective: maximize classification_accuracy Constraints: Spatial Autocorrelation exists Vegetation durability Water depth

SDM Evaluation: Changing Model • Linear Regression • Spatial model is better

SDM Evaluation: Changing measure New measure:

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

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)