Abstract

A path formula of the form 'A is followed by B in t time units'-where A and B can themselves be path formulas-is a common syntactical construct in a variety of temporal logic paradigms. We study the problem of mining for such formulas that frequently occur in time-series data in a manner that enables the discovery of complex relationships. This paper introduces a semantics that resembles categorical time series data, a syntax for such formulas that are annotated by the support for which such relationships occur in a time series, and provide algorithms that are capable of mining these relationships. We present several properties of this framework-both exploring worst case scenarios and presenting correct pruning techniques for our mining algorithms. The approach is then demonstrated with an implementation where we mine such relationships from two real-world datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-16
Number of pages8
ISBN (Electronic)9781538657621
DOIs
StatePublished - May 25 2018
Event1st International Conference on Data Intelligence and Security, ICDIS 2018 - South Padre Island, United States
Duration: Apr 8 2018Apr 10 2018

Other

Other1st International Conference on Data Intelligence and Security, ICDIS 2018
CountryUnited States
CitySouth Padre Island
Period4/8/184/10/18

Fingerprint

Time series
Temporal logic
Semantics

Keywords

  • Frequent pattern mining
  • Inductive logic
  • Modal logic
  • Temporal data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Cite this

Aleali, A., Dadfarnia, M., & Shakarian, P. (2018). Finding novel event relationships in temporal data. In Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018 (pp. 9-16). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDIS.2018.00009

Finding novel event relationships in temporal data. / Aleali, Ashkan; Dadfarnia, Mahila; Shakarian, Paulo.

Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 9-16.

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

Aleali, A, Dadfarnia, M & Shakarian, P 2018, Finding novel event relationships in temporal data. in Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018. Institute of Electrical and Electronics Engineers Inc., pp. 9-16, 1st International Conference on Data Intelligence and Security, ICDIS 2018, South Padre Island, United States, 4/8/18. https://doi.org/10.1109/ICDIS.2018.00009
Aleali A, Dadfarnia M, Shakarian P. Finding novel event relationships in temporal data. In Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 9-16 https://doi.org/10.1109/ICDIS.2018.00009
Aleali, Ashkan ; Dadfarnia, Mahila ; Shakarian, Paulo. / Finding novel event relationships in temporal data. Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 9-16
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