A Simplified and Effective Algorithm Used to Mine Similar Processes: An Illustrated Example

Authors: Min-Hsun Kuo, Yun-Shiow Chen

Abstract:

The running logs of a process hold valuable information about its executed activity behavior and generated activity logic structure. Theses informative logs can be extracted, analyzed and utilized to improve the efficiencies of the process's execution and conduction. One of the techniques used to accomplish the process improvement is called as process mining. To mine similar processes is such an improvement mission in process mining. Rather than directly mining similar processes using a single comparing coefficient or a complicate fitness function, this paper presents a simplified heuristic process mining algorithm with two similarity comparisons that are able to relatively conform the activity logic sequences (traces) of mining processes with those of a normalized (regularized) one. The relative process conformance is to find which of the mining processes match the required activity sequences and relationships, further for necessary and sufficient applications of the mined processes to process improvements. One similarity presented is defined by the relationships in terms of the number of similar activity sequences existing in different processes; another similarity expresses the degree of the similar (identical) activity sequences among the conforming processes. Since these two similarities are with respect to certain typical behavior (activity sequences) occurred in an entire process, the common problems, such as the inappropriateness of an absolute comparison and the incapability of an intrinsic information elicitation, which are often appeared in other process conforming techniques, can be solved by the relative process comparison presented in this paper. To demonstrate the potentiality of the proposed algorithm, a numerical example is illustrated.

Keywords: process mining, process similarity, artificial intelligence, process conformance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332530

References:

[1] van der Aalst, W.M.P., H.A. Reijers, A.J.M.M. Weijters, B.F. van Dongen, A.K. Alves de Medeiros, M. Song, H.M.W. Verbeek, "Business process mining: An industrial application," Information Systems, vol.32, no.5, pp.713-732, 2007.
[2] van Dongen, B. F., A. K. Alves de Medeiros and L. Wen, "Overview and Outlook of Petri Net Discovery Algorithms," in Transactions on Petri Nets and Other Models of Concurrency II, Lecture Notes in Computer Science 5460, pp.225-242, 2009.
[3] van der Aalst, W.M.P., A.K. Alves de Medeiros and A.J.M.M. Weijters, "Process equivalence: comparing two process models based on observed behavior," in 2006 International Conference on Business Process Management, Lecture Notes on Computer Science, vol.4102, pp.129-144, 2006.
[4] Tiwari, A., C.J. Turner and B. Majeed, "A review of business process mining: state-of-the-art and future trends," Business Process Management Journal, vol.14, no.1, pp.5-22, 2008.
[5] van der Aalst, W. M. P. and K. M. van Hee, "Business process redesign: A Petri-net-based approach," Computers in Industry, vol.29, no. 1-2 , pp.15-26, 1996.
[6] Salimifard, K. and M. Wright, "Petri net-based modeling of workflow systems: An overview," European Journal of Operational Research, vol.134, no.3, pp.664-676, 2001.
[7] Dang, J., A. Hedayati, K. Hampel and C. Toklu, "An ontological knowledge framework for adaptive medical workflow," Journal of Biomedical Informatics, vol.41, no.5, pp.829-836, 2008.
[8] Li, Jiafei, Dayou Liu and Bo Yang, "Process mining: extending α -Algorithm to mine duplicate tasks in process logs," in Advances in Web and Network Technologies, and Information Management, Lecture Notes in Computer Science 4537, pp. 396-407, 2007.
[9] Mruster, L., Weijters, A.J.M.M., Wil M.P. van dre AALST and Antal van den Bosch, "A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs," Data Mining and Knowledge Discovery, vol.13, no.1, pp.67-87, 2006.
[10] de Medeiros, A. K. A., A. J. M. M. Weijters and W. M. P. van der Aalst, "Genetic process mining: an experimental evaluation," Data Mining and Knowledge Discovery, vol.14, no.2, pp.245-304, 2007.
[11] Dijkman, R.M., M. Dumas, C. Ouyang, "Semantics and analysis of business process models in BPMN," Information and Software Technology, vol.50, no.12, pp.1281-1294, 2008.
[12] Wen, Lijie, Jianmin Wang, M. P. W. van der Aalst, Biqing Huang and Jiaguang Sun "A novel approach for process mining based on event types," Journal of Intelligent Information Systems, vol.32, no.2, pp.163-190, 2009.
[13] Duan, H., Qingtian Zeng, Huaiqing Wang, Sherry X. Sun and Dongming Xu, "Classification and evaluation of timed running schemas for workflow based on process mining," Journal of Systems and Software, vol.82, no.3, 2009, pp.400-410.
[14] Ho, G. T. S., H. C. W. Lau, S. K. Kwok, C. K. M. Lee and W. Ho, "Development of a co-operative distributed process mining system for quality assurance," International Journal of Production Research, vol.47, no.4, pp.883-918, 2009.
[15] Li, Jiexun, Harry Jiannan Wang, Zhu Zhang and J. Leon Zhao, "A policy-based process mining framework: mining business policy texts for discovering process models," Information Systems and E-Business Management, pp.to be published, 2009.
[16] Rozinat, A. and W.M.P. van der Aalst, "Conformance checking of processes based on monitoring real behavior," Information Systems, vol.33, no.1, pp.64-95, 2008.
[17] de Medeiros, A.K. Alves, W.M.P. van der Aalst and A.J.M.M. Weijters, "Quantifying process equivalence based on observed behavior," Data and Knowledge Engineering, vol. 64, no.1, pp.55-74, 2008.