Abstract
To understand process executed in many activities, process mining technologies are now extensively studied. However, three major problems in the current process mining techniques are identified. First, most process mining techniques mainly use local search strategy to generate process models. Second, time intervals between two actives are not considered so that patterns that are different in view of time are regarded as the same behaviors. Third, no precision evaluation measure is defined to evaluate the quality of process models. To solve these difficulties, this research proposes a time-interval process mining method. A genetic process mining algorithm with time-interval consideration is developed. Then, a precision evaluation measure is defined to evaluate the quality of the generated process models. Finally, the best process model with highest precision value is reported.
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.
Similar content being viewed by others
References
Van der Aalst, W.M.P., van Dongen, B.F.: Discovery workflow performance models from timed logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002)
Alves de Medeiros, A.K., van Dongen, B.F., van der Aalst, W.M.P., Weijters, A.J.M.M.: Process Mining: Extending the α-algorithm to Mine Short Loops. BETA Working Paper Series (WP 113), Eindhoven University of Technology, Eindhoven (2004)
Ren, C., Wen, L., Dong, J., Ding, H., Wang, W., Qiu, M.: A novel approach for process mining based on event types. In: IEEE International Conference on Services Computing (SCC 2007), vol. 4278740, pp. 721–722 (2007)
De Medeiros, A.K.A., Weijters, A.J.M.M., Van Der Aalst, W.M.P.: Genetic process mining: An experimental evaluation. Data Mining and Knowledge Discovery 14(2), 245–304 (2007)
Van Der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
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 (LNAI), vol. 2888, pp. 389–406. Springer, Heidelberg (2003)
Alves de Medeiros, A.K., 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 (WP 124), Eindhoven University of Technology, Eindhoven (2004)
Cook, J.E., Du, Z., Wolf, A.L.: Discovering Models of Behavior for Concurrent Workflows. Computers in Industry 53(3), 297–319 (2004)
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, p. 469. 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)
Cook, J.E., Wolf, A.L.: Event-based detection of concurrency. In: Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp. 35–45 (1998)
Van der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining: a research agenda. Computers in Industry 53, 231–244 (2004)
De Medeiros, A.K.A., Van Dongen, B.F., Van Der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining for ubiquitous mobile systems: An overview and a concrete algorithm. In: Baresi, L., Dustdar, S., Gall, H.C., Matera, M. (eds.) UMICS 2004. LNCS (LNAI), vol. 3272, pp. 151–165. Springer, Heidelberg (2004)
Wen, L., Van Der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Mining and Knowledge Discovery 15(2), 145–180 (2007)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsai, CY., Chen, IC. (2009). Using Genetic Process Mining Technology to Construct a Time-Interval Process Model. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-02568-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02567-9
Online ISBN: 978-3-642-02568-6
eBook Packages: Computer ScienceComputer Science (R0)