ProM

The ProM framework: A new era in process mining tool support Under the umbrella of buzzwords such as “Business Activity Monitoring” (BAM) and “Business Process Intelligence” (BPI) both academic (e.g., EMiT, Little Thumb, InWoLvE, Process Miner, and MinSoN) and commercial tools (e.g., ARIS PPM, HP BPI, and ILOG JViews) have been developed. The goal of these tools is to extract knowledge from event logs (e.g., transaction logs in an ERP system or audit trails in a WFM system), i.e., to do process mining. Unfortunately, tools use different formats for reading/storing $log$ files and present their results in different ways. This makes it difficult to use different tools on the same data set and to compare the mining results. Furthermore, some of these tools implement concepts that can be very useful in the other tools but it is often difficult to combine tools. As a result, researchers working on new process mining techniques are forced to build a mining infrastructure from scratch or test their techniques in an isolated way, disconnected from any practical applications. To overcome these kind of problems, we have developed the ProM framework, i.e., an “pluggable” environment for process mining. The framework is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas. This paper introduces the ProM framework and gives an overview of the plug-ins that have been developed.


References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Ahmed, Aishah; Koutny, Maciej; Pietkiewicz-Koutny, Marta: Synthesising elementary net systems with localities (2022)
  2. Fani Sani, Mohammadreza; van Zelst, Sebastiaan J.; van der Aalst, Wil M. P.: The impact of biased sampling of event logs on the performance of process discovery (2021)
  3. Hlomozda, D. K.; Glybovets, M. M.; Maksymets, O. M.: Automating the conversion of colored Petri nets with qualitative tokens into colored Petri nets with quantitative tokens (2018)
  4. van Zelst, S. J.; van Dongen, B. F.; van der Aalst, W. M. P.; Verbeek, H. M. W.: Discovering workflow nets using integer linear programming (2018)
  5. Kalenkova, Anna A.; Lomazova, Irina A.; van der Aalst, Wil M. P.: Process model discovery: a method based on transition system decomposition (2014)
  6. De Weerdt, Jochen; De Backer, Manu; Vanthienen, Jan; Baesens, Bart: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs (2012) ioport
  7. Solé, Marc; Carmona, Josep: Incremental process discovery (2012)
  8. Paralič, Ján; Richter, Christoph; Babič, František; Wagner, Jozef; Raček, Michal: Mirroring of knowledge practices based on user-defined patterns (2011) ioport
  9. 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 (2005)