Deriving kripke structures from time series segmentation results

Satish Tadepalli, Naren Ramakrishnan, Bud Mishra, Layne T. Watson, Richard F. Helm

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

Abstract

Kripke structures are important modeling formalisms to understand the behavior of reactive systems. We present an approach to automatically infer Kripke structures from time series datasets. Our algorithm bridges the continuous world of time profiles and the discrete symbols of Kripke structures by incorporating a segmentation algorithm as an intermediate step. This approach identifies, in an unsupervised manner, the states of the Kripke structure, the transition relation, and the properties (propositions) that hold true in each state. We demonstrate experimental results of our approach to understanding the interplay between key biological processes.

Original languageEnglish (US)
Title of host publicationProceedings - 9th International Workshop on Discrete Event Systems, WODES' 08
Pages406-411
Number of pages6
DOIs
StatePublished - 2008
Event9th International Workshop on Discrete Event Systems, WODES' 08 - Goteborg, Sweden
Duration: May 28 2008May 30 2008

Publication series

NameProceedings - 9th International Workshop on Discrete Event Systems, WODES' 08

Other

Other9th International Workshop on Discrete Event Systems, WODES' 08
CountrySweden
CityGoteborg
Period5/28/085/30/08

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering

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  • Cite this

    Tadepalli, S., Ramakrishnan, N., Mishra, B., Watson, L. T., & Helm, R. F. (2008). Deriving kripke structures from time series segmentation results. In Proceedings - 9th International Workshop on Discrete Event Systems, WODES' 08 (pp. 406-411). [4605980] (Proceedings - 9th International Workshop on Discrete Event Systems, WODES' 08). https://doi.org/10.1109/WODES.2008.4605980