Name
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. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | Fuzzy Event Sequence Identification, Fuzzy Virtual Machine, Collaborative Intelligence |
Field | DocType | ISSN |
Data mining,Data modeling,Data stream mining,Virtual machine,Web intelligence,Computer science,Fuzzy logic,Artificial intelligence,Virtual finite-state machine,Pattern matching,Collaborative intelligence,Machine learning | Conference | 1544-5615 |
Citations | PageRank | References |
1 | 0.40 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trevor P. Martin | 1 | 134 | 26.98 |
| Ben Azvine | 2 | 1 | 0.73 |