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Process Miner — A Tool for Mining Process Schemes from Event-Based Data

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Logics in Artificial Intelligence (JELIA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2424))

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Abstract

Today, process schemes are required for a lot of purposes. Extracting process schemes from event-based data is an alternative to creating them manually. Process Miner is a research prototype that can extract process schemes from event-based data. Its extracting procedure is a multistage data mining that uses a special process model. This paper outlines the main features of the tool and gives an insight into the theoretical background. Also, it describes shortly its implementation and outlines its experimental evaluation.

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References

  1. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, 1996.

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  2. J. Han and M. Kamber. Data Mining-Concepts and Techniques. Academic Press, 2001.

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  3. J. R. Quinland. C4.5-Programs for Machine Learning. Morgan Kaufmann, 1998.

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  4. G. Schimm. Generic linear business process modeling. In S. W. Liddle, H. C. Mayr, and B. Thalheim, editors, Proceedings of ER 2000, International Workshop on Conceptual Modeling Approaches for E-Business (eCOMO2000), volume 1921 of Lecture Notes in Computer Science, Salt Lake City, 2000. Springer.

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Author information

Authors and Affiliations

  1. Oldenburger Forschungs- und Entwicklungsinstitut, für Informatik-Werkzeuge und -Systeme (OFFIS), Germany

    Guido Schimm

Authors
  1. Guido Schimm
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Editor information

Editors and Affiliations

  1. DEIS, University of Calabria, P.te P. Bucci, Cubo 41C, 87036, Rende, CS, Italy

    Sergio Flesca  & Sergio Greco  & 

  2. Department of Mathematics, University of Calabria, P.te P. Bucci, Cubo 30B, 87036, Rende, CS, Italy

    Giovambattista Ianni  & Nicola Leone  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Schimm, G. (2002). Process Miner — A Tool for Mining Process Schemes from Event-Based Data. In: Flesca, S., Greco, S., Ianni, G., Leone, N. (eds) Logics in Artificial Intelligence. JELIA 2002. Lecture Notes in Computer Science(), vol 2424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45757-7_47

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  • DOI: https://doi.org/10.1007/3-540-45757-7_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44190-8

  • Online ISBN: 978-3-540-45757-2

  • eBook Packages: Springer Book Archive

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