A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs

van der Aalst, 2011

J. Nakatumba, Resource-aware business process management: analysis and support (Ph.D. thesis), Eindhoven University of Technology, ISBN: 978-90-386-3472-2, 2014.

A. Dohmen, J. Moormann, Identifying drivers of inefficiency in business processes: a DEA and data mining perspective, in: Enterprise, Business-Process and Information Systems Modeling, LNBIP, vol. 50, Springer, Berlin, Heidelberg, 2010, pp. 120–132.

L. Zeng, C. Lingenfelder, H. Lei, H. Chang, Event-driven quality of service prediction, in: Proceedings of the 8th International Conference of Service-Oriented Computing (ICSOC 2008), Lecture Notes in Computer Science, vol. 5364, Springer, Berlin, Heidelberg, 2008, pp. 147–161.

van der Aalst, 2012, Process mining put into context, IEEE Internet Comput., 16, 82, 10.1109/MIC.2012.12

W.M.P. van der Aalst, Process cubes: slicing, dicing, rolling up and drilling down event data for process mining, in: M. Song, M. Wynn, J. Liu (Eds.), Asia Pacific Conference on Business Process Management (AP-BPM 2013), LNBIP, vol. 159, Springer-Verlag, Beijing, China, 2013, pp. 1–22.

Mitchell, 1997

I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufmann Series in Data Management Systems, 3rd edition, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2011.

J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continuous features, in: Proceedings of the Twelfth International Conference on Machine Learning (ICML׳95), Morgan Kaufmann, Tahoe City, California, USA, 1995, pp. 194–202.

W.M.P. van der Aalst, H.T. Beer, B.F. van Dongen, Process mining and verification of properties: an approach based on temporal logic, in: Conference on the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE, Lecture Notes in Computer Science, vol. 3760, Springer, Berlin, Heidelberg, 2005, pp. 130–147.

van der Aalst, 2012, Replaying history on process models for conformance checking and performance analysis, WIREs Data Min. Knowl. Discov., 2, 182, 10.1002/widm.1045

M. de Leoni, W.M.P. van der Aalst, Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming, in: Proceedings of the 11th International Conference on Business Process Management (BPM׳13), Lecture Notes in Computer Science, vol. 8094, Springer-Verlag, Beijing, China, 2013, pp. 113–129.

de Leoni, 2015, An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data, Inf. Syst., 47, 258, 10.1016/j.is.2013.12.005

F.M. Maggi, C.D. Francescomarino, M. Dumas, C. Ghidini, Predictive monitoring of business processes, in: Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014), Lecture Notes in Computer Science, vol. 8484, 2014, pp. 457–472.

Ghattas, 2014, Improving business process decision making based on past experience, Decis. Support Syst., 59, 93, 10.1016/j.dss.2013.10.009

R. Conforti, M. de Leoni, M. La Rosa, W.M.P. van der Aalst, Supporting risk-informed decisions during business process execution, in: Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAISE׳13), Lecture Notes in Computer Science, vol. 7908, Springer-Verlag, Valencia, Spain, 2013, pp. 116–132.

A. Rozinat, W.M.P. van der Aalst, Decision mining in ProM, in: Proceedings of the 4th International Conference on Business Process Management (BPM׳06), Lecture Notes in Computer Science, Springer-Verlag, Vienna, Austria, 2006, pp. 420–425.

van der Aalst, 2011, Time prediction based on process mining, Inf. Syst., 36, 450, 10.1016/j.is.2010.09.001

S.J. Leemans, D. Fahland, W.M.P. van der Aalst, Discovering block-structured process models from incomplete event logs, in: Proceedings of the 35th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Net 2014), vol. 8489, Springer International Publishing, Tunis, Tunisia, 2014, pp. 91–110.

A. Kalenkova, M. de Leoni, W.M.P. van der Aalst, Discovering, analyzing and enhancing BPMN models using ProM, in: Proceedings of the Demo Sessions of the 12th International Conference on Business Process Management (BPM 2014), CEUR Workshop Proceedings, vol. 1295, CEUR-WS.org, 2014, p. 36.

M. de Leoni, W.M. van der Aalst, M. Dees, A general framework for correlating business process characteristics, in: Proceedings of the 11th Business Process Management (BPM׳14), Lecture Notes in Computer Science, vol. 8659, Springer International Publishing, Eindhoven, The Netherlands, 2014, pp. 250–266.

F. Folino, M. Guarascio, L. Pontieri, Discovering context-aware models for predicting business process performances, in: On the Move to Meaningful Internet Systems: OTM 2012, Lecture Notes in Computer Science, vol. 7565, Springer, Berlin, Heidelberg, 2012, pp. 287–304.

Lakshmanan, 2013, A Markov prediction model for data-driven semi-structured business processes, Knowl. Inf. Syst., 1

A. Kim, J. Obregon, J.-Y. Jung, Constructing decision trees from process logs for performer recommendation, in: Proceedings of 2013 Business Process Management Workshops, LNBIP, vol. 171, Springer, Beijing, China, 2014, pp. 224–236.

A. Senderovich, M. Weidlich, A. Gal, A. Mandelbaum, Mining resource scheduling protocols, in: Proceedings of the 11th Business Process Management (BPM׳14), Lecture Notes in Computer Science, vol. 8659, Springer International Publishing, Eindhoven, The Netherlands, 2014, pp. 200–216.

A. Senderovich, M. Weidlich, A. Gal, A. Mandelbaum, Queue mining – predicting delays in service processes, in: Proceedings of CAiSE, Lecture Notes in Computer Science, vol. 7565, Springer, Thessaloniki, Greece, 2014, pp. 42–57.

Fayyad, 1996, From data mining to knowledge discovery: an overview, 37

R.A. Sutrisnowati, H. Bae, J. Park, B.-H. Ha, Learning bayesian network from event logs using mutual information test, in: Proceedings of the 6th International Conference on Service-Oriented Computing and Applications (SOCA 2013), 2013, pp. 356–360.

Aamodt, 1994, Case-based reasoning, AI Commun., 7, 39, 10.3233/AIC-1994-7104

M. Polato, A. Sperduti, A. Burattin, M. de Leoni, Data-aware remaining time prediction of business process instances, in: Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), 2014.

A. Rogge-Solti, M. Weske, Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays, in: Proceedings of ICSOC, Lecture Notes in Computer Science, vol. 8274, Springer, Berlin, Germany, 2013.

Dumas, 2013

C.C. Ekanayake, M. Dumas, L. Garca-Bauelos, M. La Rosa, Slice, mine and dice: complexity-aware automated discovery of business process models, in: Proceedings of the 10th Business Process Management (BPM׳13), Lecture Notes in Computer Science, vol. 8094, Springer, Berlin, Heidelberg, 2013, pp. 49–64.

R.P.J.C. Bose, Process mining in the large: preprocessing, discovery, and diagnostics (Ph.D. thesis), Eindhoven University of Technology, Eindhoven, 2012.

G.M. Veiga, D.R. Ferreira, Understanding spaghetti models with sequence clustering for ProM, in: Business Process Management Workshops, Lecture Notes in Business Information Processing, vol. 43, Springer, Berlin, Heidelberg, 2010, pp. 92–103.

Greco, 2008, Mining taxonomies of process models, Data Knowl. Eng., 67, 74, 10.1016/j.datak.2008.06.010

de Weerdt, 2013, Active trace clustering for improved process discovery, IEEE Trans. Knowl. Data Eng., 25, 2708, 10.1109/TKDE.2013.64

M. Sole, J. Carmona, A high-level strategy for c-net discovery, in: Proceedings of the 12th International Conference on Application of Concurrency to System Design (ACSD), 2012, pp. 102–111.