Abstract
Industrial, scientific, and commercial applications use information systems to trace the execution of a business process. Relevant events are registered in massive logs and process mining techniques are used to automatically discover knowledge that reveals the execution and organization of the process instances (cases). In this paper, we investigate the use of a multi-level relational frequent pattern discovery method as a means of process mining. In order to process such massive logs we resort to a Grid-based implementation of the knowledge discovery algorithm that distributes the computation on several nodes of a Grid platform. Experiments are performed on real event logs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Cook, J.E., Wolf, A.L.: Software process validation: Quantitatively measuring the correspondence of a process to a model. ACM Trans. Softw. Eng. Methodol. 8(2), 147–176 (1999)
Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)
Li, T., Bollinger, T.: Distributed and parallel data mining on the grid. In: ARCS Workshops. LNI, vol. 41, pp. 370–379. GI (2004)
Lisi, F.A., Malerba, D.: Inducing multi-level association rules from multiple relations. Machine Learning 55(2), 175–210 (2004)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Michalski, R.S.: A theory and methodology of inductive learning, pp. 323–348 (1993)
Plotkin, G.D.: A note on inductive generalization 5, 153–163 (1970)
Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: VLDB, pp. 432–444 (1995)
van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., de Medeiros, A.K.A., Song, M., Verbeek, H.M.W.: Business process mining: An industrial application. Inf. Syst. 32(5), 713–732 (2007)
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47(2), 237–267 (2003)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The prom framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Turi, A., Appice, A., Ceci, M., Malerba, D. (2008). A Grid-Based Multi-relational Approach to Process Mining. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-540-85654-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85653-5
Online ISBN: 978-3-540-85654-2
eBook Packages: Computer ScienceComputer Science (R0)