Scalable Parallel Approximate Formulations of Multidimensional Spatial Auto-Regression Models for Spatial Data Mining

Abstract

The spatial auto-regression (SAR) model is a popular spatial data analysis technique which has been used in many applications with geo-spatial datasets. However, exact solutions for estimating SAR parameters are computationally expensive due to the need to compute all the eigen-values of a very large matrix. Therefore, serial solutions for the SAR model do not scale up to map sizes of interest to the Army. Thus, we developed the parallel approximate SAR models which can now be used by the Army to increase the accuracy and usefulness of maps, better analyze the impact of weather on the battlefield, make near-future predictions of the locations of enemy units, and increase the lethality of missiles.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA432837

Entities

People

  • Baris M. Kazar
  • David J. Lilja
  • Shashi Shekhar

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algebra
  • Battlefields
  • Chebyshev Approximations
  • Chebyshev Polynomials
  • Computational Complexity
  • Computer Programming
  • Computer Science
  • Computers
  • Computing-Related Activities
  • Data Analysis
  • Data Mining
  • High Performance Computing
  • Lethality
  • Linear Algebra
  • Polynomials

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Military Science and Technology Research and Modernization.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms