An Experimental Evaluation of Passage-Based Process Discovery

In the area of process mining, the ILP Miner is known for the fact that it always returns a Petri net that perfectly fits a given event log. Like for most process discovery algorithms, its complexity is linear in the size of the event log and exponential in the number of event classes (i.e., distinct activities). As a result, the potential gain by partitioning the event classes is much higher than the potential gain by partitioning the traces in the event log over multiple event logs. This paper proposes to use the so-called passages to split up the event classes over multiple event logs, and shows the results are for seven large real-life event logs and one artificial event log: The use of passages indeed alleviates the complexity, but much hinges on the size of the largest passage detected.

[1]  Alexander L. Wolf,et al.  Discovering models of software processes from event-based data , 1998, TSEM.

[2]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[3]  Wil M. P. van der Aalst,et al.  Genetic process mining: an experimental evaluation , 2007, Data Mining and Knowledge Discovery.

[4]  Wil M. P. van der Aalst,et al.  Rediscovering workflow models from event-based data using little thumb , 2003, Integr. Comput. Aided Eng..

[5]  Philippe Darondeau,et al.  Unbounded Petri Net Synthesis , 2003, Lectures on Concurrency and Petri Nets.

[6]  Bart Baesens,et al.  Robust Process Discovery with Artificial Negative Events , 2009, J. Mach. Learn. Res..

[7]  Boudewijn F. van Dongen,et al.  Process mining: a two-step approach to balance between underfitting and overfitting , 2008, Software & Systems Modeling.

[8]  Wil M. P. van der Aalst,et al.  Decomposing Process Mining Problems Using Passages , 2012, Petri Nets.

[9]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[10]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[11]  Perdita Stevens,et al.  Modelling Recursive Calls with UML State Diagrams , 2003, FASE.

[12]  Boudewijn F. van Dongen,et al.  Process Discovery using Integer Linear Programming , 2009, Fundamenta Informaticae.

[13]  Wil M. P. van der Aalst,et al.  Distributed Process Discovery and Conformance Checking , 2012, FASE.

[14]  Robin Bergenthum,et al.  Process Mining Based on Regions of Languages , 2007, BPM.

[15]  Josep Carmona,et al.  Divide-and-Conquer Strategies for Process Mining , 2009, BPM.

[16]  Josep Carmona,et al.  Process Mining from a Basis of State Regions , 2010, Petri Nets.

[17]  Josep Carmona,et al.  A Region-Based Algorithm for Discovering Petri Nets from Event Logs , 2008, BPM.

[18]  Wolfgang Reisig,et al.  Application and Theory of Petri Nets , 1982, Informatik-Fachberichte.

[19]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[20]  Josep Carmona,et al.  Process Mining Meets Abstract Interpretation , 2010, ECML/PKDD.