Abstract
Conformance checking techniques quantify correspondence between a process’s execution and a reference process model using event data. Alignments, used for conformance statistics, are computationally expensive for complex models and large datasets. Recent studies show accurate approximations can be achieved by selecting subsets of model behavior. This paper presents a novel approach deriving error bounds for conformance checking approximation based on arbitrary activity sequences. The proposed approach allows for the selection of relevant subsets for improved accuracy. Experimental evaluations validate its effectiveness, demonstrating enhanced accuracy compared to traditional alignment methods.
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References
Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance Checking. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-99414-7
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_15
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33606-5_19
Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Conformance checking approximation using subset selection and edit distance. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 234–251. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49435-3_15
van Zelst, S.J., Bolt, A., Hassani, M., van Dongen, B.F., van der Aalst, W.M.: Online conformance checking: relating event streams to process models using prefix-alignments. Int. J. Data Sci. Anal. 8, 1–16 (2017)
Bauer, M., van der Aa, H., Weidlich, M.: Sampling and approximation techniques for efficient process conformance checking. Inf. Syst. 104, 101666 (2020)
Awad, A., Raun, K., Weidlich, M.: Efficient approximate conformance checking using trie data structures. In: 3rd International Conference on Process Mining (ICPM), pp. 1–8 (2021)
Fani Sani, M., Gonzalez, J.J.G., van Zelst, S.J., van der Aalst, W.M.P.: Alignment approximator: a prom plug-in to approximate conformance statistics. In: Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Forum at BPM 2023, Utrecht, The Netherlands, pp. 102–106 (2023)
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2012)
van der Aalst, W.M.P.: Decomposing petri nets for process mining: a generic approach. Distrib. Parallel Databases 31(4), 471–507 (2013)
Bauer, M., Senderovich, A., Gal, A., Grunske, L., Weidlich, M.: How much event data is enough? A statistical framework for process discovery. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 239–256. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_15
Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Improving the performance of process discovery algorithms by instance selection. Comput. Sci. Inf. Syst. 17(3), 927–958 (2020)
Taymouri, F., Carmona, J.: A recursive paradigm for aligning observed behavior of large structured process models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 197–214. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_12
Padró, L., Carmona, J.: Computation of alignments of business processes through relaxation labeling and local optimal search. Inf. Syst. 104 (2022)
Fani Sani, M., Garza Gonzalez, J.J., van Zelst, S.J., van der Aalst, W.M.P.: Conformance checking approximation using simulation. In: 2nd International Conference on Process Mining, ICPM 2020, Padua, Italy, pp. 105–112. IEEE (2020)
Park, H., Jun, C.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)
van der Aalst, W.M.P., van Dongen, B., Günther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: Prom: the process mining toolkit. BPM (Demos) 489(31) (2009)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6
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Fani Sani, M., Kabierski, M., van Zelst, S.J., van der Aalst, W.M.P. (2024). Model-Independent Error Bound Estimation for Conformance Checking Approximation. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_28
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