Abstract
The availability of automatic support may sometimes determine the successful accomplishment of a process. Such a support can be provided if a model of the intended process is available. Many real-world process models are very complex. Additionally, their components might be associated to conditions that determine whether they are to be carried out or not. These conditions may be in turn very complex, involving sequential relationships that take into account the past history of the current process execution. In this landscape, writing and setting up manually the process models and conditions might be infeasible, and even standard Machine Learning approaches may be unable to infer them.
This paper presents a First-Order Logic-based approach to learn complex process models extended with conditions. It combines two powerful Inductive Logic Programming systems. The overall system was exploited to learn the daily routines of the user of a smart environment, for predicting his needs and comparing the actual situation with the expected one. In addition to proving the efficiency and effectiveness of the system, the outcomes show that complex, human-readable and interesting preconditions can be learned for the tasks involved in the process.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Mathematically, to split a number \(n\) in two numbers \(n_1\) and \(n_2\) so that \(n_1^{n_2}\) is maximum, one must take \(n_1 = n_2 = \sqrt{n}\).
- 2.
Actually, for one day the activity labels were missing, for which reason the corresponding case was removed from the dataset.
References
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. 469–483. Springer, Heidelberg (1998)
Anderson, C.R., Domingos, P., Weld, D.S.: Relational markov models and their application to adaptive web navigation. In: Hand, D., Keim, D., Ng, R. (eds.) Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002), pp. 143–152. ACM Press (2002)
Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. Technical report CU-CS-819-96, Department of Computer Science, University of Colorado (1996)
Esposito, F., Di Mauro, N., Basile, T.M.A., Ferilli, S.: Multi-dimensional relational sequence mining. Fundamenta Informaticae 89(1), 23–43 (2008)
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy theory revision: induction and abduction in inthelex. Mach. Learn. J. 38(1/2), 133–156 (2000)
Ferilli, S.: Woman: logic-based workflow learning and management. IEEE Trans. Syst. Man Cybern.: Syst. 44, 744–756 (2014)
Ferilli, S., Basile, T.M.A., Biba, M., Di Mauro, N., Esposito, F.: A general similarity framework for horn clause logic. Fundamenta Informaticæ 90(1–2), 43–46 (2009)
Ferilli, S., De Carolis, B., Redavid, D.: Logic-based incremental process mining in smart environments. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 392–401. Springer, Heidelberg (2013)
Ferilli, S., Esposito, F.: A heuristic approach to handling sequential information in incremental ILP. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 109–120. Springer, Heidelberg (2013)
Ferilli, S., Esposito, F.: A logic framework for incremental learning of process models. Fundamenta Informaticae 128, 413–443 (2013)
Gutmann, B., Kersting, K.: TildeCRF: conditional random fields for logical sequences. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 174–185. Springer, Heidelberg (2006)
Herbst, J., Karagiannis, D.: An inductive approach to the acquisition and adaptation of workflow models. In: Proceedings of the IJCAI 1999 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business, pp. 52–57 (1999)
Jacobs, N.: Relational sequence learning and user modelling (2004)
Kersting, K., De Raedt, L., Gutmann, B., Karwath, A., Landwehr, N.: Relational sequence learning. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic ILP 2007. LNCS (LNAI), vol. 4911, pp. 28–55. Springer, Heidelberg (2008)
Kersting, K., Raiko, T., Kramer, S., De Raedt, L.: Towards discovering structural signatures of protein folds based on logical hidden markov models. Technical report report00175, Institut fur Informatik, Universit at Freiburg, 13 June 2002
Dan Lee, S., De Raedt, L.: Constraint based mining of first order sequences in SeqLog. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 154–173. Springer, Heidelberg (2004)
Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)
van der Aalst, W.M.P.: The application of Petri Nets to workflow management. J. Circuits, Syst. Comput. 8, 21–66 (1998)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data. In: Hoste, V., De Pauw, G. (eds.) Proceedings of the 11th Dutch-Belgian Conference of Machine Learning (Benelearn 2001), pp. 93–100 (2001)
Acknowledgments
This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ferilli, S., De Carolis, B., Esposito, F. (2015). Learning Complex Activity Preconditions in Process Mining. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2014. Lecture Notes in Computer Science(), vol 8983. Springer, Cham. https://doi.org/10.1007/978-3-319-17876-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-17876-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17875-2
Online ISBN: 978-3-319-17876-9
eBook Packages: Computer ScienceComputer Science (R0)