Soft Hierarchical Analytics for Discrete Event Sequences

07/20/2016
Speaker(s) : Trevor Martin (Bristol University)
Over recent years, increasing quantities of data have been generated and recorded about many aspects of our lives. In cases such as internet logs, physical access logs, transaction records, email and phone records, the data consists of multiple overlapping sequences of events related to different individuals and entities. Identification and analysis of such event sequences is an important task which can be used to find similar groups, predict future behaviour and to detect anomalies. It is ideally suited to a collaborative intelligence approach, in which human analysts provide insight and interpretation, while machines perform data collection, repetitive processing and visualisation. An important aspect of this process is the common definition of terms used by humans and machines to identify and categorise similar (and dissimilar) events.
In this talk we will argue that fuzzy set theory gives a natural framework for the exchange of information, and interaction, between analysts and machines. We will describe a new approach to the definition of fuzzy hierarchies, and show how this enables event sequences to be extracted, compared and mined at different levels of resolution.
**Bio**
Trevor Martin (M’07) is a Professor of artificial intelligence at the University of Bristol, U.K. He received the B.Sc. degree in chemical physics from the University of Manchester, in 1978, and the Ph.D. degree in quantum chemistry from the University of Bristol, in 1984. Since 2001, he has been funded by British Telecommunications (BT) as a Senior Research Fellow, for his research on soft computing in intelligent information management, including areas such as the semantic Web, soft concept hierarchies, and user modeling.
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