A virtual machine for event sequence identification using fuzzy tolerance

Trevor Martin, Ben Azvine

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
261 Downloads (Pure)

Abstract

Analysing event logs and identifying multiple overlapping sequences of events is an important task in web intelligence and in other applications involving data streams. It is ideally suited to a collaborative intelligence approach, where humans provide insight and machines perform the repetitive processing and data collection. A fuzzy approach allows flexible definition of the relations which link events into a sequence. In this paper we describe a virtual machine which enables a previously published expandable sequence pattern format to be represented as virtual machine instructions, which can filter event streams and identify fuzzily related sequences.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)
Subtitle of host publication Proceedings of a meeting held 24-29 July 2016, Vancouver, British Columbia, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1080-1087
Number of pages8
ISBN (Electronic)9781509006267
ISBN (Print)9781509006274
DOIs
Publication statusPublished - Feb 2017

Publication series

NameProceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1098-7584

Keywords

  • Fuzzy Event Sequence Identification
  • Fuzzy Virtual Machine
  • Collaborative Intelligence

Fingerprint Dive into the research topics of 'A virtual machine for event sequence identification using fuzzy tolerance'. Together they form a unique fingerprint.

  • Cite this

    Martin, T., & Azvine, B. (2017). A virtual machine for event sequence identification using fuzzy tolerance. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016): Proceedings of a meeting held 24-29 July 2016, Vancouver, British Columbia, Canada (pp. 1080-1087). [7737808] (Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/FUZZ-IEEE.2016.7737808