Online Techniques for Dealing with Concept Drift in Process Mining

Concept drift is an important concern for any data analysis scenario involving temporally ordered data. In the last decade Process mining arose as a discipline that uses the logs of information systems in order to mine, analyze and enhance the process dimension. There is very little work dealing wit...

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Bibliographic Details
Published inAdvances in Intelligent Data Analysis XI pp. 90 - 102
Main Authors Carmona, Josep, Gavaldà, Ricard
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2012
SeriesLecture Notes in Computer Science
Subjects
Abstract Interpretation
Concept Drift
Convex Polyhedron
Learning Stage
Process Mining
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ISBN9783642341557
3642341551
ISSN0302-9743
1611-3349
DOI10.1007/978-3-642-34156-4_10

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Summary:Concept drift is an important concern for any data analysis scenario involving temporally ordered data. In the last decade Process mining arose as a discipline that uses the logs of information systems in order to mine, analyze and enhance the process dimension. There is very little work dealing with concept drift in process mining. In this paper we present the first online mechanism for detecting and managing concept drift, which is based on abstract interpretation and sequential sampling, together with recent learning techniques on data streams.
ISBN:9783642341557
3642341551
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-34156-4_10