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IBSM: Interval-Based Sequence Matching
University of Texas at Arlington, , .
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics , 2013Conference paper, (Refereed)
Abstract [en]

Sequences of event intervals appear in several application domains including sign language, sensor networks, medicine, human motion databases, and linguistics. Such sequences comprise events that occur at time intervals and are time stamped at their start and end time. In this paper, we propose a new method, called IBSM, for comparing such sequences. IBSM performs full sequence matching using a vector-based representation of the original sequence. At each time point an event vector is computed; hence, the original sequence is mapped to an ordered set of vectors, which we call event table. Given two sequences, their event tables are resized using bilinear interpolation, which ensures they are of the same size. The resulting event tables are then compared using the Euclidean distance. In addition, we propose two techniques for reducing the computational cost of IBSM when performing nearest neighbor search in a large database. Extensive experiments on eight real datasets show that IBSM outperforms existing state-of-the-art methods in terms of nearest neighbor classication accuracy, and by up to two orders of magnitude in terms of runtime.

Place, publisher, year, edition, pages
Society for Industrial and Applied Mathematics , 2013.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-144938ISBN: 978-1-61197-262-7 OAI: oai:DiVA.org:su-144938DiVA: diva2:1117600
Available from: 2017-06-29 Created: 2017-06-29

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