ProM 4.0: Comprehensive Support for Real Process Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4546)


This tool paper describes the functionality of ProM. Version 4.0 of ProM has been released at the end of 2006 and this version reflects recent achievements in process mining. Process mining techniques attempt to extract non-trivial and useful information from so-called “event logs”. One element of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. Control-flow discovery is an interesting and practically relevant challenge for Petri-net researchers and ProM provides an excellent platform for this. For example, the theory of regions, genetic algorithms, free-choice-net properties, etc. can be exploited to derive Petri nets based on example behavior. However, as we will show in this paper, the functionality of ProM 4.0 is not limited to control-flow discovery. ProM 4.0 also allows for the discovery of other perspectives (e.g., data and resources) and supports related techniques such as conformance checking, model extension, model transformation, verification, etc. This makes ProM a versatile tool for process analysis which is not restricted to model analysis but also includes log-based analysis.


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  1. 1.
    van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering Social Networks from Event Logs. Computer Supported Cooperative work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., Rosemann, M., Dumas, M.: Deadline-based Escalation in Process-Aware Information Systems. Decision Support Systems 43(2), 492–511 (2007)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., Rubin, V., van Dongen, B.F., Kindler, E., Günther, C.W.: Process Mining: A Two-Step Approach using Transition Systems and Regions. BPM Center Report BPM-06-30, (2006)
  4. 4.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data. and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data. Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  6. 6.
    Cortadella, J., Kishinevsky, M., Lavagno, L., Yakovlev, A.: Deriving Petri Nets from Finite Transition Systems. IEEE Transactions on Computers 47(8), 859–882 (1998)CrossRefMathSciNetGoogle Scholar
  7. 7.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: A New Era in Process Mining Tool Support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Kindler, E.: On the Semantics of EPCs: A Framework for Resolving the Vicious Circle. Data and Knowledge Engineering 56(1), 23–40 (2006)CrossRefGoogle Scholar
  9. 9.
    Rozinat, A., van der Aalst, W.M.P.: Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models. In: Bussler, C., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 163–176. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Eindhoven University of Technology, EindhovenThe Netherlands
  2. 2.University of Paderborn, PaderbornGermany

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