Scopus

eXplainable Process Mining: Conceptual Initiation and Experimental Validation

Năm XB 2025 Tạp chí / Hội thảo Lecture Notes in Networks and Systems 393–408 Đơn vị CNTT DOI / Link https://doi.org/10.1007/978-3-031-92602-0_26 ↗

Tác giả

Tóm tắt

In order to effectively do reengineering a (workflow or business) process model as a lifecycle-managing activity, it is important to secure an elaborate process mining system that is able to discover the enacted process model from an enactment event log dataset of a corresponding process model. In evaluating the process mining approaches and systems, there are two fundamental research issues; One is the discovery accuracy issue and the other is the discovery adequacy issue. The former is about the syntactical quality of the discovered process model, while the later is about the semantical quality of the discovered process model. In this paper, we initiate an innovative process mining approach, which is named as the explainable process mining approach, as a silver bullet that can be a decisive solution for both of the research issues at the same time. We also validate its practical feasibility through an operational experiment on an exemplary dataset available from the 4TU Center for Research Data. Ultimately, through the explainable process mining approach and system, it ought to be possible for the modelers and the mining engineers to effectively explain the reasons why the structural formations of control-flow gateway-activities of the discovered process models are shaped as well as stemmed from in terms of fulfilling the process reengineering works in the process lifecycle management activities.

Tài liệu tham khảo

[1] Centre for Research Data 4TU. Bpi challenges. BPM, 2012, 2013, 2014, 2015, 2016, 2017, 2018

[2] Hyun Ahn and Kwanghoon Pio Kim: A formal approach for discovering work transference networks from workflow logs. Inf. Sci. 515, 1–25 (2020)

[3] Hyun Ahn and Kwanghoon Pio Kim: Formal approach to workflow application fragmentations over cloud deployment models. Comput. Mater. Continua 67(3), 3071–3088 (2021)

[4] Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

[5] Ellis, C.A., Kim, K., Rembert, A., Wainer, J.: Investigations on stochastic information control nets. Inf. Sci. 194, 120–137 (2012)

[6] Ellis, C.A., Rembert, A.J., Kim, K.-H., Wainer, J.: Beyond workflow mining. In: International Conference on Business Process Management, pp. 49–64. Springer (2006)

[7] IEEE. Ieee standard for extensible event stream (xes) for achieving interoperability in event logs and event streams. IEEE 1849-2016 (2016)

[8] Khan, F.A., Ahmad, F., Khan, A.A., Wechtaisong, C.: Process discovery and refinement of an enterprise management system. Comput. Syst. Sci. Eng. 44(3), 2019–2032 (2023)

[9] Kim, K.: A model-driven workflow fragmentation framework for collaborative workflow architectures and systems. J. Netw. Comput. Appl. 35(1), 97–110 (2012)

[10] Kim, K., Ellis, C.A.: $$\sigma $$-algorithm: Structured workflow process mining through amalgamating temporal workcases. In: Zhou, Z.H., Li, H., Yang, Q. (eds.) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. LNCS, vol. 4426, pp. 119–130 (2007)

[11] Kim, K., Ellis, C.A.: Section II / Chapter VII. An ICN-based Workflow Model and Its Advances, Handbook of Research on BP Modeling. IGI Global, ISR, Hershey, Pennsylvania, USA (2009)

[12] Kwanghoon Pio Kim: Functional integration with process mining and process analyzing for structural and behavioral properness validation of discovered processes from event log datasets. Appl. Sci. 10(4), 1493–1517 (2020)

[13] Kwanghoon Pio Kim: A structural anomaly-trace detection approach and its implications in process mining and reengineering. IEEE Access 12, 171099–171111 (2024)

[14] Kim, K.-S.: Proportional Process Pattern Discovery and Experimental Analytics. Ph.D. thesis, Kyunghee University, Yongin-si, Gyeonggi-do, South Korea (2019)

[15] Kim, K.-S., Pham, D.-L., Kim, K.P.: $$\rho $$-algorithm: a sicn-oriented process mining framework. IEEE Access 9, 139852–139875 (2021)

[16] Kim, K.-S., Pham, D.-L., Park, Y.-I., Kim, K.P.: Experimental verification and validation of the sicn-oriented process mining algorithm and system. J. King Saud Univ. - Comput. Inf. Sci. 34(10), 9793–9813 (2022)

[17] Kim, K., Yeon, M., Jeong, B., Kim, K.: A conceptual approach for discovering proportions of disjunctive routing patterns in a business process model. KSII Trans. Internet Inf. Syst. 11(2), 1148–1161 (2017)

[18] Pio Kwanghoon Kim: Experimental analyses of temporal activity-sequencing anomalies in process mining. Appl. Sci. 13(5), 3143–3157 (2023)

[19] Kumar, A.: Business process management. Routledge (2018)

[20] Lacheheub, M.N., Hameurlain, N., Maamri, R.: Resources consumption analysis of business process services in cloud computing using petri net. J. King Saud Univ. - Comput. Inf. Sci. 32, 408–418 (2020)

[21] Lee, Y., Chang, I.-S., Suseong, O., Nam, Y., Chae, Y., Choi, G., Park, G.: Hierarchical flow-based anomaly detection model for motor gearbox defect detection. KSII Trans. Internet Inf. Syst. 17(6), 1516–1529 (2023)

[22] Li, J., Liu, D.,  Yang, B.: Process mining: extending $$\alpha $$-algorithm to mine duplicate tasks in process logs. In: Chen-Chuan Chang, K., Wei Wang, Lei Chen, Clarence A. Ellis, Ching-Hsien Hsu, Ah Chung Tsoi, and Haixun Wang, editors, Advances in Web and Network Technologies, and Information Management, pp. 396–407. Springer, Heidelberg (2007)

[23] Nguyen, T.-H., Kim, K.P.: Discovering structural formations of multi-level and compound control-flow gateways from process logs. IEEE Access 11, 128826–128842 (2023)

[24] Nguyen, T.-H., Kim, K.-S., Pham, D.-L., Kim, K.P.: A web-based system for business process discovery: leveraging the sicn-oriented process mining algorithm with django, cytoscape, and graphviz. KSII Trans. Internet Inf. Syst. 18(8), 2316–2332 (2024)

[25] Schimm, G.: Mining exact models of concurrent workflows. Comput. Ind. 53(3), 265–281 (2004)

[26] Van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., Van Der Aalst, W.M.P.: The prom framework: a new era in process mining tool support. In: International Conference on Application and Theory of Petri Nets, pp. 444–454. Springer (2005)

[27] Wang, X., Cao, Z., Zhan, R., Bai, M., Ma, Q., Li, G.: Density-based outlier detection in multi-dimensional datasets. KSII Trans. Internet Inf. Syst. 16(12), 3815–3835 (2022)

[28] Yeon, M.-S., Lee, Y.-K., Pham, D.-L., Kim, W.P.: Experimental verification on human-centric network-based resource allocation approaches for process-aware information systems. IEEE Access 10, 23342–23354 (2022)

← Quay lại danh sách bài báo