Understanding Spaghetti Models with Sequence Clustering for ProM

  • Gabriel M. Veiga
  • Diogo R. Ferreira
Conference paper

DOI: 10.1007/978-3-642-12186-9_10

Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 43)
Cite this paper as:
Veiga G.M., Ferreira D.R. (2010) Understanding Spaghetti Models with Sequence Clustering for ProM. In: Rinderle-Ma S., Sadiq S., Leymann F. (eds) Business Process Management Workshops. BPM 2009. Lecture Notes in Business Information Processing, vol 43. Springer, Berlin, Heidelberg

Abstract

The goal of process mining is to discover process models from event logs. However, for processes that are not well structured and have a lot of diverse behavior, existing process mining techniques generate highly complex models that are often difficult to understand; these are called spaghetti models. One way to try to understand these models is to divide the log into clusters in order to analyze reduced sets of cases. However, the amount of noise and ad-hoc behavior present in real-world logs still poses a problem, as this type of behavior interferes with the clustering and complicates the models of the generated clusters, affecting the discovery of patterns. In this paper we present an approach that aims at overcoming these difficulties by extracting only the useful data and presenting it in an understandable manner. The solution has been implemented in ProM and is divided in two stages: preprocessing and sequence clustering. We illustrate the approach in a case study where it becomes possible to identify behavioral patterns even in the presence of very diverse and confusing behavior.

Keywords

Process Mining Preprocessing Sequence Clustering ProM Markov Chains Event Logs Hierarchical Clustering Process Models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gabriel M. Veiga
    • 1
  • Diogo R. Ferreira
    • 1
  1. 1.IST – Technical University of LisbonPorto SalvoPortugal

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