A virtual machine for event sequence identification using fuzzy tolerance
Trevor Martin, Ben Azvine · 2016
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.