Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Laxman, Srivatsan and Sastry, PS and Unnikrishnan, KP (2007) Discovering Frequent Generalized Episodes When Events Persist for Different Durations. In: IEEE Transactions on Knowledge and Data Engineering, 19 (9). pp. 1188-1201.

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Abstract

This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined, which extends this framework by incorporating event duration constraints explicitly into the pattern's definition. This new formalism facilitates extension of the technique of episodes discovery to applications where data appears as a sequence of events that persist for different durations (rather than being instantaneous). We present efficient algorithms for episode discovery in this new framework. Through extensive simulations, we show the expressive power of the new formalism. We also show how the duration constraint possibilities can be used as a design choice to properly focus the episode discovery process. Finally, we briefly discuss some interesting results obtained on data from manufacturing plants of General Motors.

Item Type:	Journal Article
Publication:	IEEE Transactions on Knowledge and Data Engineering
Publisher:	IEEE
Additional Information:	Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Data mining;sequential data;frequent episodes;efficient algorithms;event durations.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	25 Sep 2008 05:14
Last Modified:	19 Sep 2010 04:49
URI:	http://eprints.iisc.ac.in/id/eprint/15899

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