Data Modeling for predictive behavior hypothesis formation and testing

Holger M. Jaenisch; James W. Handley; Marvin H. Barnett; Richard Esslinger; David A. Grover; Jeffrey P. Faucheux; Kenneth Lamkin

doi:10.1117/12.666411

18 April 2006 Data modeling for predictive behavior hypothesis formation and testing

Holger M. Jaenisch, James W. Handley, Marvin H. Barnett, Richard Esslinger, David A. Grover, Jeffrey P. Faucheux, Kenneth Lamkin

Author Affiliations +

Proceedings Volume 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006; 62410P (2006) https://doi.org/10.1117/12.666411
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

This paper presents a novel hypothesis analysis tool building on QUEST and DANCER. Unique is the ability to convert cause/effect relationships into analytical equation transfer functions for exploitation. In this the third phase of our work, we derive Data Models for each unique word and its ontological associated unique words. We form a classical control theory transfer function using the associated words as the input vector and the assigned unique word as the output vector. Each transfer function model can be tested against new evidence to yield new output. Additionally, conjectured output can be passed through the inverse model to predict the requisite case observations required to yield the conjectured output. Hypotheses are tested using circumstantial evidence, notional similarity, evidential strength, and plausibility to determine if they are supported or rejected. Examples of solving for evidence links are provided from tool execution.

Citation Download Citation

Holger M. Jaenisch, James W. Handley, Marvin H. Barnett, Richard Esslinger, David A. Grover, Jeffrey P. Faucheux, and Kenneth Lamkin "Data modeling for predictive behavior hypothesis formation and testing", Proc. SPIE 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006, 62410P (18 April 2006); https://doi.org/10.1117/12.666411

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