Process Management techniques are useful in domains where the availability of a (formal) process model can be leveraged to monitor, supervise, and control a production process. While their classical application is in the business and industrial fields, other domains may profitably exploit Process Management techniques. Some of these domains (e.g., people’s behavior, General Game Playing) are much more flexible and variable than classical ones, and, thus, raise the problem of predicting which activities will be carried out next, a problem that is not so compelling in classical fields. When the process model is learned automatically from examples of process executions, which is the task of Process Mining, the prediction performance may also provide indirect indications on the correctness and reliability of the learned model. This paper proposes and compares two strategies for activity prediction using the WoMan framework for workflow management. The former proved to be able to handle complex processes, the latter is based on the classic and consolidated Naïve Bayes approach. An experimental validation allows us to draw considerations on the pros and cons of each, used both in isolation and in combination.
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It can be of any representation of time points (e.g., milliseconds from process case start, date-time in YYYYMMDDHHMMSS format, progressive integers, etc).
In First-order logic, the p/n notation is used to denote an n-ary predicate symbol p.
Note that this is a different meaning than in Petri Nets.
In this perspective the term resource assumes a general meaning, which is not relative to the event resource R that executes an activity in a process case.
It is a multiset since an activity could occur more than once.
This is a relevant difference with respect to the heuristic approach, where there is no distinction about different occurrences of the same task. Our expectation is that, by considering such a more fine-grained information, predictions may be more accurate.
In each case, a relationship between a γ and a 𝜃 occurs at most once, if any.
Agrawal, R., Gunopulos, D., Leymann, F. (1998). Mining process models from workflow logs. In Proceedings of the 6th International Conference on Extending Database Technology (EDBT). https://doi.org/10.1007/BFb0101003.
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D. (2014). Completion time and next activity prediction of processes using sequential pattern mining. In International Conference on Discovery Science (pp. 49–61): Springer. https://doi.org/10.1007/978-3-319-11812-3_5.
Ceri, S., Gottlob, G., Tanca, L. (1990). Logic Programming and Databases. Verlag: Springer. https://doi.org/10.1007/978-3-642-83952-8.
Cook, J., & Wolf, A. (1996). Discovering models of software processes from event-based data. Tech. Rep CU-CS-819-96, Department of Computer Science, University of Colorado. https://doi.org/10.1145/287000.287001.
Coradeschi, S., Cesta, A., Cortellessa, G., Coraci, L., Gonzalez, J., Karlsson, L., Furfari, F., Loutfi, A., Orlandini, A., Palumbo, F., Pecora, F., von Rump, S., Štimec, U.J., Tslund, B. (2013). Giraffplus: Combining social interaction and long term monitoring for promoting independent living. In Proceedings of the 6th international conference on human system interaction (HSI) (pp. 578–585): IEEE. https://doi.org/10.1109/HSI.2013.6577883.
Ferilli, S., & Esposito, F. (2013). A logic framework for incremental learning of process models. Fundamenta Informaticae, 128, 413–443. https://doi.org/10.3233/FI-2013-951.
Ferilli, S. (2014). Woman: Logic-based workflow learning and management. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(6), 744–756. https://doi.org/10.1109/TSMC.2013.2273310.
Ferilli, S., Esposito, F., Redavid, D., Angelastro, S. (2016). Predicting process behavior in woman. In AI*IA 2016: Advances in Artificial Intelligence - XVth International Conference of the Italian Association for Artificial Intelligence, Genova, Italy, November 29 - December 1, 2016. https://doi.org/10.1007/978-3-319-49130-1_23(pp. 308–320): Proceedings.
Ferilli, S., Esposito, F., Redavid, D., Angelastro, S. (2017a). Extended process models for activity prediction. In Kryszkiewicz, M., Appice, A., Slezak, D., Rybinski, H., Skowron, A., Rás Z (Eds.) Foundations of Intelligent Systems, Springer, Lecture Notes in Artificial Intelligence, (Vol. 10352 pp. 194–208). https://doi.org/10.1007/978-3-319-60438-1_36.
Ferilli, S., Redavid, D., Angelastro, S. (2017b). Activity prediction in process management using the woman framework. In Perner, P. (Ed.) Advances in Data Mining. Applications and Theoretical Aspects, Springer, Lecture Notes in Artificial Intelligence, (Vol. 10357 pp. 194–208). https://doi.org/10.1007/978-3-319-62701-4_15.
Greco, G., Guzzo, A., Pontieri, L. (2005). Mining hierarchies of models: From abstract views to concrete specifications. Business Process Management, 3649, 32–47. https://doi.org/10.1007/11538394_3.
Herbst, J., & Karagiannis, D. (1998). Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In 1998 Proceedings. Ninth International Workshop on Database and expert systems applications (pp. 745–752): IEEE. https://doi.org/10.1109/DEXA.1998.707491.
Herbst, J., & Karagiannis, D. (1999). An inductive approach to the acquisition and adaptation of workflow models. In Proceedings of the IJCAI’99 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business (pp 52–57), Stockholm, Sweden.
IEEE Task Force on Process Mining. (2012). Process mining manifesto. In Business Process Management Workshops, Lecture Notes in Business Information Processing (vol. 99, pp. 169–194). https://doi.org/10.1007/978-3-642-28108-2_19.
Lai, M. (2015). Giraffe: Using deep reinforcement learning to play chess. CoRR 1509.01549.
Lloyd, J.W. (1987). Foundations of Logic Programming, 2nd edn., Springer, Berlin. https://doi.org/10.1007/978-3-642-96826-2.
Mitchell, T.M. (1997). Machine Learning, 1st edn. New York: McGraw-Hill, Inc. https://doi.org/10.1007/978-3-662-12405-5.
Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8(4), 295–318. https://doi.org/10.1007/BF03037089.
Oshri, B., & Khandwala, N. (2016). Predicting moves in chess using convolutional neural networks. In Stanford University Course Project Reports – CS231n: Convolutional Neural Networks for Visual Recognition. https://cs231n.stanford.edu/reports/ConvChess.pdf.
Pesic, M., & van der Aalst, W.M.P. (2006). A declarative approach for flexible business processes management. In Proceedings of the 2006 international conference on Business Process Management Workshops, BPM’06 (pp. 169–180): Springer-Verlag. https://doi.org/10.1007/11837862_18.
Schonenberg, H., Weber, B., van Dongen, B., van der Aalst, W. (2008). Supporting flexible processes through recommendations based on history. In: International Conference on Business Process Management (pp. 51–66): Springer. https://doi.org/10.1007/978-3-540-85758-7_7.
van der Aalst, W. (1998). The application of petri nets to workflow management. The Journal of Circuits, Systems and Computers, 8, 21–66. https://doi.org/10.1142/S0218126698000043.
van der Aalst, W., Weijters, T., Maruster, L. (2004). Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16, 1128–1142. https://doi.org/10.1109/TKDE.2004.47.
van der Aalst, W.M., De Medeiros, A.A., Weijters, A. (2005). Genetic process mining. In International Conference on Application and Theory of Petri Nets (pp 48–69): Springer. https://doi.org/10.1007/11494744_5.
Weijters, A., & van der Aalst, W. (2001). Rediscovering workflow models from event-based data. In Proceedings of the 11th dutch-belgian conference of machine learning (benelearn 2001) (pp. 93–100).
Wen, L., Wang, J., Sun, J. (2006). Detecting implicit dependencies between tasks from event logs. Frontiers of WWW Research and Development-APWeb 2006 (pp. 591–603). https://doi.org/10.1007/11610113_52.
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Ferilli, S., Angelastro, S. Activity prediction in process mining using the WoMan framework. J Intell Inf Syst 53, 93–112 (2019). https://doi.org/10.1007/s10844-019-00543-2
- Process mining
- Activity prediction
- Process model