Abstract
The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
van der Aalst, W.M.P.: Business Process Management Demystified: A Tutorial on Models, Systems and Standards for Workflow Management. In: Desel, J., Reisig, W., Rozenberg, G. (eds.) Lectures on Concurrency and Petri Nets. LNCS, vol. 3098, pp. 1–65. Springer, Berlin (2004)
van der Aalst, W.M.P., Song, M.: Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Berlin (2004)
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)
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)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Sixth International Conference on Extending Database Technology, pp. 469–483 (1998)
Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)
Dehnert, J., van der Aalst, W.M.P.: Bridging the Gap Between Business Models and Workflow Specifications. International Journal of Cooperative Information Systems 13(3), 289–332 (2004)
Ehrenfeucht, A., Rozenberg, G.: Partial (Set) 2-Structures — Part 1 and Part 2. Acta Informatica 27(4), 315–368 (1989)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing. Springer, Berlin (2003)
van Hee, K., Sidorova, N., Voorhoeve, M.: Soundness and Separability of Workflow Nets in the Stepwise Refinement Approach. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 335–354. Springer, Berlin (2003)
Herbst, J.: A Machine Learning Approach to Workflow Management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Berlin (2000)
Mauch, H.: Evolving Petri Nets with a Genetic Algorithm. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1810–1811. Springer, Berlin (2003)
de Medeiros, A.K.A., van der Aalst, W.M.P., Weijters, A.J.M.M.: Workflow Mining: Current Status and Future Directions. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) CoopIS 2003, DOA 2003, and ODBASE 2003. LNCS, vol. 2888, pp. 389–406. Springer, Berlin (2003)
de Medeiros, A.K.A., Weijters, A.J.M.M., van der Aalst, W.M.P.: Using Genetic Algorithms to Mine Process Models: Representation, Operators and Results. BETA Working Paper Series, vol. WP 124. Eindhoven University of Technology, Eindhoven (2004)
Moore, J.H., Hahn, L.W.: Petri Net Modeling of High-Order Genetic Systems Using Grammatical Evolution. BioSystems 72(1-2), 177–186 (2003)
Moore, J.H., Hahn, L.W.: An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 63–72. Springer, Berlin (2004)
Reddy, J.P., Kumanan, S., Chetty, O.V.K.: Application of Petri Nets and a Genetic Algorithm to Multi-Mode Multi-Resource Constrained Project Scheduling. International Journal of Advanced Manufacturing Technology 17(4), 305–314 (2001)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M. (2005). Genetic Process Mining. In: Ciardo, G., Darondeau, P. (eds) Applications and Theory of Petri Nets 2005. ICATPN 2005. Lecture Notes in Computer Science, vol 3536. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494744_5
Download citation
DOI: https://doi.org/10.1007/11494744_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26301-2
Online ISBN: 978-3-540-31559-9
eBook Packages: Computer ScienceComputer Science (R0)