Abstract
Process mining techniques assist users to automatically infer process models from event logs. However, the result of process model driven by traditional process mining technique may conflict with the knowledge of users due to some real conditions, i.e. alternative activity is selected due to equipment breakdown. First, the use of heuristics may detect inconsistencies caused by bad guess. Second, extraction of all possible ordering of events reflects historical observation that sometimes hinders users to obtain an ideal process model since the activity has some event types. Yet, the current process mining approach is not totally compatible with some aspects such as extra logs behavior and soundness of process model when the process model changes according to user requirements. This paper presents a method for synthesizing activity proximity from event logs in the area of process mining. The method derives a bounded graph that covers the extra-behavior of an event log according to user’s domain knowledge. Another important property is that it produces a graph with considering the proximity among activities that still contains the original behavior of the event log based on event types. The methods described in this paper have been implemented in ProM framework and tested on a set of real process examples.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02922-1_10
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kawalek, P., Kueng, P.: The Usefulness of Process Models: A lifecycle Description of how Process Models are used in Modern Organisations. In: Proceedings of the Second CAiSE/IFIPS. 1 International Workshop on Evaluation of Modelling Methods in Systems Analysis and Design, pp. 1–12 (1997)
Weijters, A.J.M.M., van der Aalst, W.M.P., de Medeiros, A.K.A.: Process mining with heuristics miner algorithm. Eindhoven University of Technology, Eindhoven (2006)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process Discovery Using Integer Linear Programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008)
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)
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
Rozinat, A., de Medeiros, A.K.A., Gunther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: Towards an evaluation framework for process mining algorithm. Eindhoven University of Technology, Eindhoven (2007)
Koren, Y., North, S.C., Volinsky, C.: Measuring and Extracting Proximity Graphs in Networks. Journal ACM Transactions on Knowledge Discovery from Data (TKDD) 1(3) (2007)
Gunther, H.O., Kim, K.H.: Container Terminals and Automated Transport Systems: Logistics Control Issues and Quantitative Decision Support. Springer, Berlin (2005)
Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process Mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007)
van Dongen, B.F., van der Aalst, W.M.P.: A Meta Model for Process Mining Data. In: Casto, J., Teniente, E. (eds.) Proceedings of the CAiSE 2005 Workshops (EMOI-INTEROP Workshop), Porto, Portugal, pp. 309–320 (2005)
Greco, G., Guzzo, A., Pontieri, L.: Process Discovery via Precedence Constraints. In: ECAI (2011)
Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305–1340 (2009)
de Amo, S., Furtado, D.A.: First-order temporal pattern mining with regular expression constraints. Data & Knowledge Engineering 62, 401–420 (2007)
Guns, T., Nijssen, S., De Raedt, L.: Itemset mining: A constraint programming perspective. Artificial Intelligence 175, 1951–1983 (2001)
Barba, I., Weber, B., Valle, C.D., Jimenez-Ramirez, A.: User recommendations for the optimized execution of business processes. Data & Knowledge Engineering 86, 61–84 (2013)
Rashidi, H., Tsang, E.P.K.: Novel Constraints satisfaction models for optimization problems in container terminals. Applied Mathematical Modelling 37, 3601–3634 (2013)
Džeroski, S.: Inductive Databases and Constraint-Based Data Mining. In: Jäschke, R. (ed.) ICFCA 2011. LNCS, vol. 6628, pp. 1–17. Springer, Heidelberg (2011)
Salvagnin, D.: Constraint Programming Techniques for Mixed Integer Linear Programming, Padova (2008)
Yahya, B.N., Bae, H., Bae, J., Liu, L.: Tool Support for Process Modeling using Proximity Score Measurement. International Journal of Innovative Computing, Information and Control 8(7B), 5381–5399 (2012)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time Based Prediction on Process Mining. Information Systems 36(2), 450–475 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yahya, B.N., Bae, H., Sul, So., Wu, JZ. (2013). Process Discovery by Synthesizing Activity Proximity and User’s Domain Knowledge. In: Song, M., Wynn, M.T., Liu, J. (eds) Asia Pacific Business Process Management. AP-BPM 2013. Lecture Notes in Business Information Processing, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-319-02922-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-02922-1_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02921-4
Online ISBN: 978-3-319-02922-1
eBook Packages: Computer ScienceComputer Science (R0)