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

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

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