Online Techniques for Dealing with Concept Drift in Process Mining

  • Josep Carmona
  • Ricard Gavaldà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


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.


Process Mining Convex Polyhedron Abstract Interpretation Concept Drift Learning Stage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Josep Carmona
    • 1
  • Ricard Gavaldà
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

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