Abstract
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.
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References
Akbarinia, R., Pacitti, E., Valduriez, P.: Best Position Algorithms for Top-k Queries. In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, pp. 495–506 (2007)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender Systems Survey. Knowledge-Based Systems 46, 109–132 (2013)
Campolongo, F., Saltelli, A., Cariboni, J.: From Screening to Quantitative Sensitivity Analysis. A Unified Approach. Computer Physics Communications 182(4), 978–988 (2011)
Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: Proceedings of the Twentieth Symposium on Principles of Database Systems, PODS 2001, pp. 102–113. ACM, New York (2001)
Mısır, M., Sebag, M.: Algorithm Selection as a Collaborative Filtering Problem. Technical report, INRIA (2013)
Morris, M.D.: Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics 33(2), 161–174 (1991)
O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using Case-Based Reasoning in an Algorithm Portfolio for Constraint Solving. In: Irish Conference on Artificial Intelligence and Cognitive Science (2008)
Rice, J.R.: The Algorithm Selection Problem. Adv. in Computers 15, 65–118 (1976)
Rozinat, A., de Medeiros, A.K.A., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: Towards an Evaluation Framework for Process Mining Algorithms. Technical Report 224, Eindhoven University of Technology (2006)
Rozinat, A., van der Aalst, W.M.P.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33(1), 64–95 (2008)
Sobol, I.M.: Uniformly Distributed Sequences With an Additional Uniform Property. USSR Computational Mathematics and Mathematical Physics 16(5), 236–242 (1976)
Su, X., Khoshgoftaar, T.M.: A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence (2009)
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
van den Broucke, S., Delvaux, C., Freitas, J., Rogova, T., Vanthienen, J., Baesens, B.: Uncovering the Relationship between Event Log Characteristics and Process Discovery Techniques. In: Proceedings of the 9th Workshop on Business Process Intelligence, BPI 2013 (2013)
van den Broucke, S., Weerdt, J.D., Baesens, B., Vanthienen, J.: A Comprehensive Benchmarking Framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: IEEE Symposium on Computational Intelligence and Data Mining, Grand Copthorne Hotel, Singapore. IEEE (2013)
Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: ProM 6: The Process Mining Toolkit. In: Demo at the 8th International Conference on Business Process Management. CEUR-WS, vol. 615, pp. 34–39 (2010)
Wang, J., Wong, R.K., Ding, J., Guo, Q., Wen, L.: On Recommendation of Process Mining Algorithms. In: 2012 IEEE 19th International Conference on Web Services (ICWS), pp. 311–318 (2012)
Weber, P., Bordbar, B., Tino, P., Majeed, B.: A Framework for Comparing Process Mining Algorithms. In: IEEE GCC Conference and Exhibition, pp. 625–628 (2011)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible Heuristics Miner (FHM). In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, Paris, France. IEEE (2011)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: Portfolio-Based Algorithm Selection for SAT. J. of Artif. Intelligence Research 32(1), 565–606 (2008)
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Ribeiro, J., Carmona, J., Mısır, M., Sebag, M. (2014). A Recommender System for Process Discovery. In: Sadiq, S., Soffer, P., Völzer, H. (eds) Business Process Management. BPM 2014. Lecture Notes in Computer Science, vol 8659. Springer, Cham. https://doi.org/10.1007/978-3-319-10172-9_5
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DOI: https://doi.org/10.1007/978-3-319-10172-9_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10171-2
Online ISBN: 978-3-319-10172-9
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