Abstract
Learning humans’ behavior from activity logs requires choosing an adequate machine learning technique regarding the situation at hand. This choice impacts significantly results reliability. In this paper, Hidden Markov Models (HMMs) are used to build intentional process models (Maps) from activity logs. Since HMMs parameters require to be learned, the main contribution of this paper is to compare supervised and unsupervised learning approaches of HMMs. After a theoretical comparison of both approaches, they are applied on two controlled experiments to compare the Maps thereby obtained. The results demonstrate using supervised learning leads to a poor performance because it imposes binding conditions in terms of data labeling, introduces inherent humans’ biases, provides unreliable results in the absence of ground truth, etc. Instead, unsupervised learning obtains efficient Maps with a higher performance and lower humans’ effort.
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References
Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM TOSEM 7(3), 215–249 (1998)
van der Aalst, W.: Process mining: Discovery, conformance and enhancement of business processes. Springer, Heidelberg (2011)
Mirbel, I., Ralyté, J.: Situational method engineering: combining assembly-based and roadmap-driven approaches. Requirements Engineering 11(1), 58–78 (2006)
Jarke, M., Pohl, K.: Establishing visions in context: Towards a model of requirements processes. In: ICIS, pp. 23–34 (1993)
Rolland, C., Salinesi, C.: Modeling goals and reasoning with them. In: Engineering and Managing Software Requirements, pp. 189–217. Springer, Heidelberg (2005)
Plihon, V., Rolland, C.: Modelling ways-of-working. In: Iivari, J., Rossi, M., Lyytinen, K. (eds.) CAiSE 1995. LNCS, vol. 932, pp. 126–139. Springer, Heidelberg (1995)
Rolland, C., Prakash, N., Benjamen, A.: A multi-model view of process modelling. Requirements Engineering 4(4), 169–187 (1999)
Rolland, C.: Modeling the requirements engineering process. In: Information Modelling and Knowledge Bases, Budapest, Hungary, pp. 85–96 (May 1993)
Thevenet, L.-H., Salinesi, C.: Aligning is to organizations strategy: The instal method. In: Krogstie, J., Opdahl, A. L., Sindre, G. (eds.) CAiSE 2007. LNCS, vol. 4495, pp. 203–217. Springer, Heidelberg (2007)
Salinesi, C., Rolland, C.: Fitting business models to system functionality exploring the fitness relationship. In: Eder, J., Missikoff, M. (eds.) CAiSE 2003. LNCS, vol. 2681, pp. 647–664. Springer, Heidelberg (2003)
Rolland, C., Kirsch-Pinheiro, M., Souveyet, C.: An intentional approach to service engineering. IEEE Transactions on Services Computing 3(4), 292–305 (2010)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Proceedings of the IEEE, vol. 77(2), pp. 257–286 (1989)
Khodabandelou, G., Hug, C., Deneckere, R., Salinesi, C.: Unsupervised discovery of intentional process models from event logs. Submitted at: The 11th Working Conference on Mining Software Repositories, MSR 2014 (2014)
Dardenne, A., Van Lamsweerde, A., Fickas, S.: Goal-directed requirements acquisition. Science of Computer Programming 20(1), 3–50 (1993)
Yu, E.: Modelling strategic relationships for process reengineering. Social Modeling for Requirements Engineering 11 (2011)
Soffer, P., Rolland, C.: Combining intention-oriented and state-based process modeling. In: Delcambre, L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 47–62. Springer, Heidelberg (2005)
Gray, W.D., John, B.E., Atwood, M.E.: The precis of project ernestine or an overview of a validation of goms. In: Human Factors in CS, ACM, pp. 307–312 (1992)
Hayashi, M.: Hidden markov models to identify pilot instrument scanning and attention patterns. IEEE Systems, Man and Cybernetics, 2889–2896 (2003)
Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science 185(4157), 1124–1131 (1974)
Khodabandelou, G., Hug, C., Deneckere, R., Salinesi, C.: Supervised intentional process models discovery using hidden markov models. In: Proceed. of 7th Int. Conf. on Research Challenges in Information Science (2013)
Burnham, K.P., Anderson, D.R.: Model selection and multi-model inference: A practical information-theoretic approach. Springer, Heidelberg (2002)
Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. The Royal Statistical Society, 1–38 (1977)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)
Rozinat, A., Veloso, M., van der Aalst, W.M.: Evaluating the quality of discovered process models. In: 2nd WS on the Induction of Process Models, Citeseer (2008)
Herbst, J., Karagiannis, D.: Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In: Proceed. 9th Intl. Workshop on Database and Expert Systems Applications. IEEE (1998)
van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic process mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005)
Cook, J.E., Wolf, A.L.: Automating process discovery through event-data analysis. In: Software Engineering, ICSE 1995, pp. 73–73. IEEE (1995)
Das, S., Mozer, M.C.: A unified gradient-descent/clustering architecture for finite state machine induction. In: NIPS, pp. 19–26. Morgan Kaufmann (1994)
Biermann, A.W., Feldman, J.A.: On the synthesis of finite-state machines from samples of their behavior. IEEE Transactions on Computers, 592–597 (1972)
Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state markov chains. The Annals of Mathematical Statistics 37(6), 1554–1563 (1966)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 467–483. Springer, Heidelberg (1998)
EUT: Prom, http://www.processmining.org/prom/start (November 2013)
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Khodabandelou, G., Hug, C., Deneckère, R., Salinesi, C. (2014). Supervised vs. Unsupervised Learning for Intentional Process Model Discovery. In: Bider, I., et al. Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2014 2014. Lecture Notes in Business Information Processing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43745-2_15
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