Paper Info
Title
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
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 .
Year
DOI
Venue
2007
10.1109/TKDE.2007.1055
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
different duration,episode discovery process,different durations,episodes discovery,new temporal pattern,new formalism facilitates extension,new framework,frequent generalized,new formalism,frequent episode discovery,episode discovery,generalized episode,hidden markov models,time frequency analysis,electrical engineering,correlation,expressive power,algorithm design and analysis,data mining
Data mining,Algorithm design,Design choice,Computer science,Information extraction,Time–frequency analysis,Artificial intelligence,Knowledge extraction,Formalism (philosophy),Business process discovery,Hidden Markov model,Machine learning
Journal
Volume
Issue
ISSN
19
9
1041-4347
Citations 
PageRank 
References 
33
1.59
8
Authors
3
Name
Order
Citations
PageRank
Srivatsan Laxman142121.65
P. Sastry223512.27
K. P. Unnikrishnan329923.21