Capturing emergent behavior in multi-response systems through data trend mining

Conrad S. Tucker, Harrison M. Kim

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

Abstract

This paper presents a novel approach to capture emerging systems behavior involving multiple performance criteria. Due to the interactions that exist among systems, engineers may be faced with a multi-objective design space that current single response data mining models do not capture. We aim to address this challenge by proposing a Multi-Response Trend Mining algorithm that simultaneously predicts multiple performance objectives by identifying the time series behavior of the individual systems. The proposed approach is a departure from conventional data mining approaches that are often limited to evaluating single response variables in a given static data set. The resulting system level predictions will serve as performance targets for next generation systems design efforts. The Multi- Response Trend Mining model can then be integrated with multi-objective engineering models during the systems design and analysis phase so that engineering design solutions better reflect emerging system performance trends. A vehicle design data set from the UC Irvine Machine Learning Repository is used to validate the proposed methodology and highlight the need for multi-response predictive algorithms in systems design.

Original languageEnglish (US)
Title of host publication13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
DOIs
StatePublished - Dec 1 2010
Event13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 - Ft. Worth, TX, United States
Duration: Sep 13 2010Sep 15 2010

Publication series

Name13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010

Other

Other13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010
CountryUnited States
CityFt. Worth, TX
Period9/13/109/15/10

Fingerprint

Data mining
Systems analysis
Learning systems
Time series
Engineers

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Mechanical Engineering

Cite this

Tucker, C. S., & Kim, H. M. (2010). Capturing emergent behavior in multi-response systems through data trend mining. In 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010 (13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010). https://doi.org/10.2514/6.2010-9322
Tucker, Conrad S. ; Kim, Harrison M. / Capturing emergent behavior in multi-response systems through data trend mining. 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010. 2010. (13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010).
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Tucker, CS & Kim, HM 2010, Capturing emergent behavior in multi-response systems through data trend mining. in 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010. 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010, 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010, Ft. Worth, TX, United States, 9/13/10. https://doi.org/10.2514/6.2010-9322

Capturing emergent behavior in multi-response systems through data trend mining. / Tucker, Conrad S.; Kim, Harrison M.

13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010. 2010. (13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010).

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

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Tucker CS, Kim HM. Capturing emergent behavior in multi-response systems through data trend mining. In 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010. 2010. (13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010). https://doi.org/10.2514/6.2010-9322