Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

  • Wil M. P. van der Aalst
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 159)


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.


OLAP Process Mining Big Data Process Discovery Conformance Checking 


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  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    van der Aalst, W.M.P.: Decomposing Petri Nets for Process Mining: A Generic Approach. Distributed and Parallel Databases 31(4), 471–507 (2013)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    van der Aalst, W.M.P., Dustdar, S.: Process Mining Put into Context. IEEE Internet Computing 16(1), 82–86 (2012)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    Agrawal, R., Shafer, J.C.: Parallel Mining of Association Rules. IEEE Transactions on Knowledge and Data Engineering 8(6), 962–969 (1996)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Heuristics Miners for Streaming Event Data. CoRR, abs/1212.6383 (2012)Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    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)CrossRefGoogle Scholar
  25. 25.
    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)CrossRefGoogle Scholar
  26. 26.
    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)CrossRefGoogle Scholar
  27. 27.
    Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM Sigmod Record 26(1), 65–74 (1997)CrossRefGoogle Scholar
  28. 28.
    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)CrossRefGoogle Scholar
  29. 29.
    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)CrossRefGoogle Scholar
  30. 30.
    Gaaloul, W., Gaaloul, K., Bhiri, S., Haller, A., Hauswirth, M.: Log-Based Transactional Workflow Mining. Distributed and Parallel Databases 25(3), 193–240 (2009)CrossRefGoogle Scholar
  31. 31.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305–1340 (2009)MathSciNetzbMATHGoogle Scholar
  32. 32.
    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)CrossRefGoogle Scholar
  33. 33.
    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)CrossRefGoogle Scholar
  34. 34.
    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)CrossRefGoogle Scholar
  35. 35.
    Hilbert, M., Lopez, P.: The World’s Technological Capacity to Store, Communicate, and Compute Information. Science 332(6025), 60–65 (2011)CrossRefGoogle Scholar
  36. 36.
    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)Google Scholar
  37. 37.
    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)CrossRefGoogle Scholar
  38. 38.
    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)CrossRefGoogle Scholar
  39. 39.
    Mamaliga, T.: Realizing a Process Cube Allowing for the Comparison of Event Data. Master’s thesis, Eindhoven University of Technology, Eindhoven (2013)Google Scholar
  40. 40.
    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)Google Scholar
  41. 41.
    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)MathSciNetCrossRefGoogle Scholar
  42. 42.
    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)CrossRefGoogle Scholar
  43. 43.
    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)Google Scholar
  44. 44.
    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)CrossRefGoogle Scholar
  45. 45.
    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)CrossRefGoogle Scholar
  46. 46.
    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)CrossRefGoogle Scholar
  47. 47.
    La Rosa, M., Dumas, M., ter Hofstede, A., Mendling, J.: Configurable Multi-Perspective Business Process Models. Information Systems 36(2), 313–340 (2011)CrossRefGoogle Scholar
  48. 48.
    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)Google Scholar
  49. 49.
    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)CrossRefGoogle Scholar
  50. 50.
    Rosemann, M., van der Aalst, W.M.P.: A Configurable Reference Modelling Language. Information Systems 32(1), 1–23 (2007)CrossRefGoogle Scholar
  51. 51.
    Rozinat, A., van der Aalst, W.M.P.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  52. 52.
    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)Google Scholar
  53. 53.
    Sheth, A.: A New Landscape for Distributed and Parallel Data Management. Distributed and Parallel Databases 30(2), 101–103 (2012)CrossRefGoogle Scholar
  54. 54.
    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)CrossRefGoogle Scholar
  55. 55.
    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)Google Scholar
  56. 56.
    Song, M., van der Aalst, W.M.P.: Towards Comprehensive Support for Organizational Mining. Decision Support Systems 46(1), 300–317 (2008)CrossRefGoogle Scholar
  57. 57.
    Vanhatalo, J., Völzer, H., Koehler, J.: The Refined Process Structure Tree. Data and Knowledge Engineering 68(9), 793–818 (2009)CrossRefGoogle Scholar
  58. 58.
    Verbeek, H.M.W., van der Aalst, W.M.P.: Decomposing Replay Problems: A Case Study. BPM Center Report BPM-13-09, (2013)Google Scholar
  59. 59.
    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)Google Scholar
  60. 60.
    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)Google Scholar
  61. 61.
    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)MathSciNetzbMATHGoogle Scholar
  62. 62.
    Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23, 69–101 (1996)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wil M. P. van der Aalst
    • 1
    • 2
    • 3
  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Business Process Management DisciplineQueensland University of TechnologyBrisbaneAustralia
  3. 3.International Laboratory of Process-Aware Information SystemsNational Research University Higher School of EconomicsMoscowRussia

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