@inproceedings{f14901ca98004e5d881bbef28e35a7fe,
title = "A virtual machine for event sequence identification using fuzzy tolerance",
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.",
keywords = "Fuzzy Event Sequence Identification, Fuzzy Virtual Machine, Collaborative Intelligence",
author = "Trevor Martin and Ben Azvine",
year = "2017",
month = feb,
doi = "10.1109/FUZZ-IEEE.2016.7737808",
language = "English",
isbn = "9781509006274",
series = "Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1080--1087",
booktitle = "2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)",
address = "United States",
}