Abstract
Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.
Keywords
- OLAP
- Process Mining
- Big Data
- Process Discovery
- Conformance Checking
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02922-1_10
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References
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
van der Aalst, W.M.P.: Decomposing Process Mining Problems Using Passages. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 72–91. Springer, Heidelberg (2012)
van der Aalst, W.M.P.: Distributed Process Discovery and Conformance Checking. In: de Lara, J., Zisman, A. (eds.) FASE 2012. LNCS, vol. 7212, pp. 1–25. Springer, Heidelberg (2012)
van der Aalst, W.M.P.: A General Divide and Conquer Approach for Process Mining. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems (FedCSIS 2013), pp. 1–10. IEEE Computer Society (2013)
van der Aalst, W.M.P.: Decomposing Petri Nets for Process Mining: A Generic Approach. Distributed and Parallel Databases 31(4), 471–507 (2013)
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Replaying History on Process Models for Conformance Checking and Performance Analysis. WIREs Data Mining and Knowledge Discovery 2(2), 182–192 (2012)
van der Aalst, W.M.P., Basten, T.: Identifying Commonalities and Differences in Object Life Cycles using Behavioral Inheritance. In: Colom, J.-M., Koutny, M. (eds.) ICATPN 2001. LNCS, vol. 2075, pp. 32–52. Springer, Heidelberg (2001)
van der Aalst, W.M.P., Basten, T.: Inheritance of Workflows: An Approach to Tackling Problems Related to Change. Theoretical Computer Science 270(1-2), 125–203 (2002)
van der Aalst, W.M.P., Dustdar, S.: Process Mining Put into Context. IEEE Internet Computing 16(1), 82–86 (2012)
van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering Social Networks from Event Logs. Computer Supported Cooperative Work 14(6), 549–593 (2005)
van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process Mining: A Two-Step Approach to Balance Between Underfitting and Overfitting. Software and Systems Modeling 9(1), 87–111 (2010)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Adriansyah, A., van Dongen, B., van der Aalst, W.M.P.: Conformance Checking using Cost-Based Fitness Analysis. In: Chi, C.H., Johnson, P. (eds.) IEEE International Enterprise Computing Conference (EDOC 2011), pp. 55–64. IEEE Computer Society (2011)
Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Towards Robust Conformance Checking. In: zur Muehlen, M., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 122–133. Springer, Heidelberg (2011)
Adriansyah, A., Sidorova, N., van Dongen, B.F.: Cost-based Fitness in Conformance Checking. In: International Conference on Application of Concurrency to System Design (ACSD 2011), pp. 57–66. IEEE Computer Society (2011)
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)
Agrawal, R., Shafer, J.C.: Parallel Mining of Association Rules. IEEE Transactions on Knowledge and Data Engineering 8(6), 962–969 (1996)
Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process Mining Based on Regions of Languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007)
Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling Concept Drift in Process Mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Towards cross-organizational process mining in collections of process models and their executions. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part II. LNBIP, vol. 100, pp. 2–13. Springer, Heidelberg (2012)
Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Heuristics Miners for Streaming Event Data. CoRR, abs/1212.6383 (2012)
Calders, T., Guenther, C., Pechenizkiy, M., Rozinat, A.: Using Minimum Description Length for Process Mining. In: ACM Symposium on Applied Computing (SAC 2009), pp. 1451–1455. ACM Press (2009)
Cannataro, M., Congiusta, A., Pugliese, A., Talia, D., Trunfio, P.: Distributed Data Mining on Grids: Services, Tools, and Applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(6), 2451–2465 (2004)
Carmona, J., Cortadella, J.: Process Mining Meets Abstract Interpretation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 184–199. Springer, Heidelberg (2010)
Carmona, J., Cortadella, J., Kishinevsky, M.: A Region-Based Algorithm for Discovering Petri Nets from Event Logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008)
Carmona, J., Gavaldà, R.: Online techniques for dealing with concept drift in process mining. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 90–102. Springer, Heidelberg (2012)
Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM Sigmod Record 26(1), 65–74 (1997)
Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)
Cook, J.E., Wolf, A.L.: Software Process Validation: Quantitatively Measuring the Correspondence of a Process to a Model. ACM Transactions on Software Engineering and Methodology 8(2), 147–176 (1999)
Gaaloul, W., Gaaloul, K., Bhiri, S., Haller, A., Hauswirth, M.: Log-Based Transactional Workflow Mining. Distributed and Parallel Databases 25(3), 193–240 (2009)
Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305–1340 (2009)
Gottschalk, F., van der Aalst, W.M.P., Jansen-Vullers, M.H., La Rosa, M.: Configurable Workflow Models. International Journal of Cooperative Information Systems 17(2), 177–221 (2008)
Gottschalk, F., Wagemakers, T.A.C., Jansen-Vullers, M.H., van der Aalst, W.M.P., La Rosa, M.: Configurable Process Models: Experiences From a Municipality Case Study. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 486–500. Springer, Heidelberg (2009)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy Mining: Adaptive Process Simplification Based on Multi-perspective Metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)
Hilbert, M., Lopez, P.: The World’s Technological Capacity to Store, Communicate, and Compute Information. Science 332(6025), 60–65 (2011)
IEEE Task Force on Process Mining. Process Mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. LNBIP, vol. 99, pp. 169–194. Springer, Berlin (2012)
van Leeuwen, M., Siebes, A.: StreamKrimp: Detecting Change in Data Streams. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 672–687. Springer, Heidelberg (2008)
Li, C., Reichert, M., Wombacher, A.: The MINADEPT Clustering Approach for Discovering Reference Process Models Out of Process Variants. International Journal of Cooperative Information Systems 19(3-4), 159–203 (2010)
Mamaliga, T.: Realizing a Process Cube Allowing for the Comparison of Event Data. Master’s thesis, Eindhoven University of Technology, Eindhoven (2013)
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011)
Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic Process Mining: An Experimental Evaluation. Data Mining and Knowledge Discovery 14(2), 245–304 (2007)
Muñoz-Gama, J., Carmona, J.: A Fresh Look at Precision in Process Conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010)
Munoz-Gama, J., Carmona, J.: Enhancing Precision in Process Conformance: Stability, Confidence and Severity. In: Chawla, N., King, I., Sperduti, A. (eds.) IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), Paris, France, pp. 184–191. IEEE (April 2011)
Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Conformance Checking in the Large: Partitioning and Topology. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 130–145. Springer, Heidelberg (2013)
Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Hierarchical Conformance Checking of Process Models Based on Event Logs. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 291–310. Springer, Heidelberg (2013)
Ribeiro, J.T.S., Weijters, A.J.M.M.: Event Cube: Another Perspective on Business Processes. In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 274–283. Springer, Heidelberg (2011)
La Rosa, M., Dumas, M., ter Hofstede, A., Mendling, J.: Configurable Multi-Perspective Business Process Models. Information Systems 36(2), 313–340 (2011)
La Rosa, M., Dumas, M., Uba, R., Dijkman, R.M.: Business Process Model Merging: An Approach to Business Process Consolidation. ACM Transactions on Software Engineering and Methodology 22(2) (2012)
La Rosa, M., Reijers, H.A., van der Aalst, W.M.P., Dijkman, R.M., Mendling, J., Dumas, M., Garcia-Banuelos, L.: APROMORE: An Advanced Process Model Repository. Expert Systems With Applications 38(6), 7029–7040 (2011)
Rosemann, M., van der Aalst, W.M.P.: A Configurable Reference Modelling Language. Information Systems 32(1), 1–23 (2007)
Rozinat, A., van der Aalst, W.M.P.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33(1), 64–95 (2008)
Schnieders, A., Puhlmann, F.: Variability Mechanisms in E-Business Process Families. In: Abramowicz, W., Mayr, H.C. (eds.) Proceedings of the 9th International Conference on Business Information Systems (BIS 2006). LNI, vol. 85, pp. 583–601. GI (2006)
Sheth, A.: A New Landscape for Distributed and Parallel Data Management. Distributed and Parallel Databases 30(2), 101–103 (2012)
Solé, M., Carmona, J.: Process Mining from a Basis of Regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010)
Song, M., van der Aalst, W.M.P.: Supporting Process Mining by Showing Events at a Glance. In: Chari, K., Kumar, A. (eds.) Proceedings of 17th Annual Workshop on Information Technologies and Systems (WITS 2007), Montreal, Canada, pp. 139–145 (December 2007)
Song, M., van der Aalst, W.M.P.: Towards Comprehensive Support for Organizational Mining. Decision Support Systems 46(1), 300–317 (2008)
Vanhatalo, J., Völzer, H., Koehler, J.: The Refined Process Structure Tree. Data and Knowledge Engineering 68(9), 793–818 (2009)
Verbeek, H.M.W., van der Aalst, W.M.P.: Decomposing Replay Problems: A Case Study. BPM Center Report BPM-13-09, BPMcenter.org (2013)
Weerdt, J.D., Backer, M.D., Vanthienen, J., Baesens, B.: A Robust F-measure for Evaluating Discovered Process Models. In: Chawla, N., King, I., Sperduti, A. (eds.) IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), Paris, France, pp. 148–155. IEEE (April 2011)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process Discovery using Integer Linear Programming. Fundamenta Informaticae 94, 387–412 (2010)
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23, 69–101 (1996)
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van der Aalst, W.M.P. (2013). Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining. In: Song, M., Wynn, M.T., Liu, J. (eds) Asia Pacific Business Process Management. AP-BPM 2013. Lecture Notes in Business Information Processing, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-319-02922-1_1
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