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A Classification Algorithm for Process Sequences Based on Markov Chains and Bayesian Networks

  • Conference paper
Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6276))

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Abstract

Companies in the customer-centric service sector deal with thousands of business processes on a daily basis. As different tasks progress along the process sequence, the process owners are interested in three key questions: which task could be the next in the remaining process path, how and when will the process finish? In this paper, we focus mainly on the first two question, the prediction of the length of the process path is part of further research. We propose a classification method based on Markov chains and Bayesian networks for predicting such properties like the remaining process flow, especially the next task in the process path, and the class of the process. This approach is applied and tested on real-world data, showing some interesting results and hints for further research.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2007)

    Google Scholar 

  2. Van der Aalst, W., van Hee, K.: Workflow Management: Models, Methods, and Systems. MIT Press, Cambridge (2002)

    Google Scholar 

  3. Van der Aalst, W., van Dongen, B., Herbst, J.: Workflow mining: a survey of issues and approaches. SData and Knowledge Engineering 2(47), 237–267 (2003)

    Google Scholar 

  4. Bonifati, A., Casati, F., Dayal, U., Shan, M.: Warehousing workflow data: Challenges and opportunities. In: Procs. of VLDB 2001, Rome, Italy (September 2001)

    Google Scholar 

  5. Panagos, E., Rabinovich, M.: Escalations in workflow management systems. In: Procs. of DART 1997, Rockville, Maryland (November 1997)

    Google Scholar 

  6. Norris, J.: Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, Cambridge (1998)

    Google Scholar 

  7. Lawler, G.F.: Introduction to Stochastic Processes. Crc Pr Inc., Boca Raton (2006)

    MATH  Google Scholar 

  8. Jensen, F.V., Nielsen, T.: Bayesian Networks and Decision Graphs. Springer, Berlin (2007)

    Book  MATH  Google Scholar 

  9. Tajima, F., Nei, M.: Estimation of evolutionary distance between nucleotide sequences. Molecular Biology and Evolution 1, 269–285 (1984)

    Google Scholar 

  10. Miller, W., Myers, E.W.: Sequence comparison with concave weighting functions. Bulletin of Mathematical Biology 50(2), 97–120 (1988)

    MATH  MathSciNet  Google Scholar 

  11. Gotoh, O.: An improved algorithm for matching biological sequences. Journal of Molecular Biology 162, 705–708 (1982)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, D-38302, Wolfenbuettel, Germany

    Katharina Tschumitschew & Frank Klawonn

  2. BT Group, Chief Technology Office, Research and Venturing Intelligent Systems Research Centre Adastral Park, Orion Building pp1/12, Ipswich, IP5 3RE, UK

    Detlef Nauck

Authors
  1. Katharina Tschumitschew
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  2. Detlef Nauck
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  3. Frank Klawonn
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Editor information

Editors and Affiliations

  1. School of Engineering, The Parade, Cardiff University, CF24 3AA, Cardiff, UK

    Rossitza Setchi

  2. Dept. of Computer Science and Software Engineering, BUckingham Building, Lion Terrace, University of Portsmouth, PO1 3HE, Portsmouth, UK

    Ivan Jordanov

  3. KES International, 145-157, St. John Street, EC1V 4PY, London, UK

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, ,, Adelaide, Mawson Lakes Campus, 5095, SA, Australia

    Lakhmi C. Jain

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© 2010 Springer-Verlag Berlin Heidelberg

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Tschumitschew, K., Nauck, D., Klawonn, F. (2010). A Classification Algorithm for Process Sequences Based on Markov Chains and Bayesian Networks. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-15387-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15386-0

  • Online ISBN: 978-3-642-15387-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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