Abstract
The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a semi-supervised problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process models which are not only simple, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on numerical abstract domains and Satisfiability Modulo Theories (SMT), and can be combined with any process discovery technique. As an example, we show in detail how to supervise a recent technique that uses numerical abstract domains. Experiments performed in our prototype implementation show the effectiveness of the techniques and the ability to improve the results produced by selected discovery techniques.
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References
van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM, pp. 310–317 (2011)
Ferreira, H., Ferreira, D.: An integrated life cycle for workflow management based on learning and planning. International Journal of Cooperative Information Systems 15(4), 485–505 (2006)
Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Inducing declarative logic-based models from labeled traces. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 344–359. Springer, Heidelberg (2007)
Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. Inductive Logic Programming, 132–146 (2008)
Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: the sciff framework. ACM Transactions on Computational Logic (TOCL) 9(4), 29 (2008)
Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305–1340 (2009)
Rockafellar, R.T.: Convex Analysis. Princeton University Press (1970)
Nieuwenhuis, R., Oliveras, A., Tinelli, C.: Solving SAT and SAT modulo theories: From an abstract Davis-Putnam-Logemann-Loveland procedure to DPLL( t). J. ACM 53(6), 937–977 (2006)
Carmona, J., Cortadella, J.: Process discovery algorithms using numerical abstract domains. IEEE Trans. Knowl. Data Eng. 26(12), 3064–3076 (2014)
Fukuda, K., Picozzi, S., Avis, D.: On canonical representations of convex polyhedra. In: Proc. of the First International Congress of Mathematical Software, pp. 350–360 (2002)
Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–580 (1989)
vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)
de Moura, L., Bjørner, N.S.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008)
Carmona, J., Cortadella, J., Kishinevsky, M.: New region-based algorithms for deriving bounded Petri nets. IEEE Trans. Computers 59(3), 371–384 (2010)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008)
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. Inf. Syst. E-Business Management 13(1), 37–67 (2015)
Maruster, L., Weijters, A., van der Aalst, W., van den Bosch, A.: A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs. Data Mining and Knowledge Discovery 13(1), 67–87 (2006)
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Ponce-de-León, H., Carmona, J., vanden Broucke, S.K.L.M. (2015). Incorporating Negative Information in Process Discovery. In: Motahari-Nezhad, H., Recker, J., Weidlich, M. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9253. Springer, Cham. https://doi.org/10.1007/978-3-319-23063-4_8
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DOI: https://doi.org/10.1007/978-3-319-23063-4_8
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