Marking Time in Sequence Mining

Sequence mining is often conducted over static and temporal datasets as well as over collections of events (episodes). More recently, there has also been a focus on the mining of streaming data. However, while many sequences are associated with absolute time values, most sequence mining routines treat time in a relative sense, only returning patterns that can be described in terms of Allen-style relationships (or simpler). In this work we investigate the accommodation of timing marks within the sequence mining process. The paper discusses the opportunities presented and the problems that may be encountered and presents a novel algorithm, INTEMTM , that provides support for timing marks. This enables sequences to be examined not only in respect of the order and occurrence of tokens but also in terms of pace. Algorithmic considerations are discussed and an example provided for the case of polled sensor data.