A non-numerical predictive model for asymmetric analysis

Michael L. Valenzuela, Chuan Feng, Praneel Reddy, Faisal Momen, Jerzy W. Rozenblit, Brian Ten Eyck, Ferenc Szidarovszky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Predicting asymmetric threats (e.g., terrorist events) is becoming ever more important. Prior works have focused on tactical, statistical, and data-fusion systems. The thrust of our work has been the development of a non-numerical predictive model for amplifying intelligence analysts' recognition of emergent threats. The intelligence community uses a Template schema for assessing courses of action. Our predictive model processes non-numerical data to arrive at automated assessment and confidence scores for these Templates. The predictive model is traceable, transparent, and utilizes Human-in-the-Loop data-fusion. For future work, this predictive model will be further enhanced with behavioral filtering. Behavioral filtering adjusts the assessment and confidence of the predictions by intelligently evaluating characteristic behavioral data. This non-numerical predictive model has been tested and verified in the Asymmetric Threat Response and Analysis Program (ATRAP).

Original languageEnglish (US)
Title of host publication17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2010
Pages311-315
Number of pages5
DOIs
StatePublished - 2010
Event17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2010 - Oxford, United Kingdom
Duration: Mar 22 2010Mar 26 2010

Publication series

Name17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2010

Other

Other17th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2010
Country/TerritoryUnited Kingdom
CityOxford
Period3/22/103/26/10

Keywords

  • ATRAP
  • Data-fusion
  • Non-numerical
  • Prediction
  • Template

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications

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