Abstract
The practice of process mining concerns the reconstruction of complete process flow diagrams from data logs. In an increasingly complex business environment, there is a need for managers to understand the processes they already have in place. Process mining facilitates the automatic reconstruction of a flowchart description based on a set of execution traces. The process mining approach featured in this paper utilises a Genetic Programming approach to process mining using a graph representation. The results from a number of experiments are detailed in this paper and point to its potential as a practical tool for use in industry. The principles investigated in this approach form a core component of future research by the authors in the area of process disparity identification.
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
Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.J. (ed.) EDBT 1998, vol. 1377, pp. 469–490. Springer, Heidelberg (1998)
Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic Process Mining: An Experimental Evaluation. Journal of Data Mining and Knowledge Discovery 14(2), 245–304 (2007)
Alves de Medeiros, A.K.: Genetic Process Mining, PhD Thesis, Eindhoven Technical University, Eindhoven, The Netherlands (2006)
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)
Desel, J.: Process Modeling Using Petri Nets. In: Dumas, M., van der Aalst, W.M.P., ter Hofstede, A.H.M. (eds.) Process Aware Information Systems, Hoboken, pp. 147–176. Wiley, Chichester (2005)
Herbst, J.: Ein induktiver Ansatz zur Akquisition und Adaption von Workow-Modellen, PhD thesis, Universitat Ulm (2001)
JGraph (2009), http://www.jgraph.org/ (accessed May 31, 2009)
JGraphT (2009), http://www.jgrapht.org/ (accessed May 31, 2009)
Turner, C.J., Tiwari, A., Mehnen, J.: A Genetic Programming Approach to Business Process Mining. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Atlanta, USA, July 12-16, pp. 1307–1314 (2008)
van der Aalst, W.M.P.: Process Mining: A Research Agenda. Computers In Industry 53, 231–244 (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. Journal of Data & Knowledge Engineering 47, 237–267 (2003)
van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
van Dongen, B.F., Alves de Medeiros, A.K., Verbeek, H.M.W., Weijters, A.J.M.M., 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)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data Using Little Thumb. Integral Computer-Aided Engineering 10(2), 151–162 (2003)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Process Mining: Discovering Workflow Models from Event Based Data. In: Kröse, B., Rijke, M., Schreiber, G., Someren, M. (eds.) Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence, Amsterdam, The Netherlands, pp. 283–290 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Turner, C.J., Tiwari, A., Mehnen, J. (2010). Mining Process Flowcharts from Business Data: An Evolutionary Approach. In: Huang, G.Q., Mak, K.L., Maropoulos, P.G. (eds) Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology. Advances in Intelligent and Soft Computing, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10430-5_82
Download citation
DOI: https://doi.org/10.1007/978-3-642-10430-5_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10429-9
Online ISBN: 978-3-642-10430-5
eBook Packages: EngineeringEngineering (R0)