Abstract
Although contemporary information systems are intensively utilized in enterprises, their actual impact in automating complex business process is still constrained by the difficulties coincided in design phase. In this study, a hybrid data analysis methodology to business process modeling that is based on using from-to chart is further enhanced to discover connection types and include in the process model. From-to chart is basically used as the front-end to figure out the observed transitions among the activities in event logs. The derived raw patterns are converted into activity sequence on from-to chart by using Genetic Algorithms. Finally a process model including AND/OR connection types is constructed on the basis of this activity sequence.
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References
Măruşter, L., Weijters, A.J.M.M.T., van der Aalst, W.M.P., van den Bosch, A.: Process mining: Discovering direct successors in process logs. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 364–373. Springer, Heidelberg (2002)
Esgin, E., Senkul, P.: Hybrid Approach to Process Mining: Finding Immediate Successors of a Process by Using From-to Chart. In: Int. Conf. on Machine Learning and Applications, pp. 664–668. IEEE Computer Society, Los Alamitos (2009)
Esgin, E., Senkul, P., Cimenbicer, C.: A hybrid approach for process mining: Using from-to chart arranged by genetic algorithms. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 178–186. Springer, Heidelberg (2010)
Apple, J.M.: Material Handling Systems Design. The Ronald Press Company, New York (1972)
van der Aalst, W.M.P., Gunther, C., Recker, J., Reichert, M.: Using Process Mining to Analyze and Improve Process Flexibility. In: 7th Workshop on BPMDS 2006, CAiSE 2006 Workshop (2006)
Gunther, C.W., van de Aalst, W.M.P.: Process Mining in Case Handling Systems. In: Proc. PRIMIUM Sub Conference at the Multikonferenz Wirtschaftsinformatik (2006)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (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)
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)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. Transaction on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Dianati, M., Song, I., Treiber, M.: An Introduction to Genetic Algorithms and Evaluation Stragies. Univ. of Waterloo, Canada (2004)
Sarker, B.R., Wilbert, E.W., Hogg, G.R.: Locating Sets of Identical Machines in a Linear Layout. Annals of Operations Research 77, 183–207 (1998)
Weijters, A.J.M.M., van der Aalst, W.M.P., Mederios, A.K.A.: Process Mining with the HeuristicMiner Algorithm. Paper presented at the BETA Working Paper Series, WP 166, Eindhoven University of Technology (2006)
van der Aalst, W.M.P., Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, T.A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data & Knowledge Engineering 47(2), 237–267 (2003)
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Esgin, E., Senkul, P. (2011). Extracting Connection Types in Process Models Discovered by Using From-to Chart Based Approach. In: Mehrotra, K.G., Mohan, C., Oh, J.C., Varshney, P.K., Ali, M. (eds) Developing Concepts in Applied Intelligence. Studies in Computational Intelligence, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21332-8_9
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DOI: https://doi.org/10.1007/978-3-642-21332-8_9
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