Abstract
In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples.
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
Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Towards Robust Conformance Checking. In: Proceedings of the 6th Workshop on Business Process Intelligence, BPI 2010 (2010)
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. SIGMOD Rec. 26, 65–74 (1997)
Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: a multi-dimensional framework for graph data analysis. Knowl. Inf. Syst. 21, 41–63 (2009)
Gunther, C.W.: Process Mining in Flexible Environments. PhD thesis, Eindhoven University of Technology, Eindhoven (2009)
Han, J., Kamber, M.: Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. Elsevier (2006)
Li, X., Han, J.: Mining approximate top-k subspace anomalies in multi-dimensional time-series data. In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, pp. 447–458. VLDB Endowment (2007)
Liu, M., Rundensteiner, E., Greenfield, K., Gupta, C., Wang, S., Ari, I., Mehta, A.: E-Cube: Multi-dimensional event sequence processing using concept and pattern hierarchies. In: International Conference on Data Engineering, pp. 1097–1100 (2010)
Mansmann, S., Neumuth, T., Scholl, M.H.: Multidimensional data modeling for business process analysis. In: Parent, C., Schewe, K.-D., Storey, V.C., Thalheim, B. (eds.) ER 2007. LNCS, vol. 4801, pp. 23–38. Springer, Heidelberg (2007)
Monakova, G., Leymann, F.: Workflow ART. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010, Part I. LNCS, vol. 6426, pp. 376–393. Springer, Heidelberg (2010)
Rozinat, A.: Process Mining: Conformance and Extension. PhD thesis, Eindhoven University of Technology, Eindhoven (2010)
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, 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)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible Heuristics Miner (FHM). In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011. IEEE, Paris (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ribeiro, J.T.S., Weijters, A.J.M.M. (2011). Event Cube: Another Perspective on Business Processes. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25109-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-25109-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25108-5
Online ISBN: 978-3-642-25109-2
eBook Packages: Computer ScienceComputer Science (R0)