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 language | English |
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Title of host publication | 2016 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 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1080-1087 |
Number of pages | 8 |
ISBN (Electronic) | 9781509006267 |
ISBN (Print) | 9781509006274 |
DOIs | |
Publication status | Published - Feb 2017 |
Publication series
Name | Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 1098-7584 |
Keywords
- Fuzzy Event Sequence Identification
- Fuzzy Virtual Machine
- Collaborative Intelligence
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Professor Trevor P Martin
- School of Engineering Mathematics and Technology - Professor of Artificial Intelligence
- Intelligent Systems Laboratory
Person: Academic , Member