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Efficient and Customisable Declarative Process Mining with SQL

  • Stefan Schönig
  • Andreas Rogge-Solti
  • Cristina Cabanillas
  • Stefan Jablonski
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer from performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, RelationalXES, a relational database architecture for storing event log data, has been introduced. In this paper, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. By leveraging database performance technology, the mining procedure is fast without limiting itself to detecting certain control-flow constraints. Queries can be customised and cover process perspectives beyond control flow, e.g., organisational aspects. We evaluated the performance and the capabilities of our approach with regard to several real-life event logs.

Keywords

Declarative process mining Relational databases SQL 

References

  1. 1.
    Jablonski, S.: MOBILE: a modular workflow model and architecture. In: Working Conference on Dynamic Modelling and Information Systems (1994)Google Scholar
  2. 2.
    van der Aalst, W., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci. Res. Dev. 23(2), 99–113 (2009)CrossRefGoogle Scholar
  3. 3.
    Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., Reijers, H.: Imperative versus declarative process modeling languages: an empirical investigation. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. LNBIP, vol. 99, pp. 383–394. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Vaculín, R., Hull, R., Heath, T., Cochran, C., Nigam, A., Sukaviriya, P.: Declarative business artifact centric modeling of decision and knowledge intensive business processes. In: EDOC, pp. 151–160 (2011)Google Scholar
  5. 5.
    Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Hildebrandt, T., Mukkamala, R.R., Slaats, T., Zanitti, F.: Contracts for cross-organizational workflows as timed dynamic condition response graphs. J. Logic Algebraic Program. 82(5), 164–185 (2013)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Zeising, M., Schönig, S., Jablonski, S.: Towards a common platform for the support of routine, agile business processes. In: Collaborative Computing: Networking, Applications and Worksharing (2014)Google Scholar
  8. 8.
    Maggi, F.M., Mooij, A., van der Aalst, W.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199 (2011)Google Scholar
  9. 9.
    Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM Trans. Manage. Inf. Syst. 5(4), 24:1–24:37 (2015)CrossRefGoogle Scholar
  10. 10.
    Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: Mining the organisational perspective in agile business processes. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) BPMDS 2015 and EMMSAD 2015. LNBIP, vol. 214, pp. 37–52. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  11. 11.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: Efficient discovery of understandable declarative process models from event logs. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 270–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Westergaard, M., Stahl, C., Reijers, H.: UnconstrainedMiner: efficient discovery of generalized declarative process models. BPM Center Report, No. BPM-13-28 (2013)Google Scholar
  13. 13.
    Schönig, S.: SQL Queries for Declarative Process Mining on Event Logs of Relational Databases. arXiv preprint. arXiv: 1512.00196 (2015)
  14. 14.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  15. 15.
    Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    van Dongen, B.F., Shabani, S.: Relational XES: data management for process mining. In: CAiSE Forum, pp. 169–176 (2015)Google Scholar
  17. 17.
    Bussler, C.: Organisationsverwaltung in Workflow-Management-Systemen. Dt. Univ.-Verlag (1998)Google Scholar
  18. 18.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: A knowledge-based integrated approach for discovering and repairing declare maps. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 433–448. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Bose, R.P.J.C., Maggi, F.M., van der Aalst, W.M.P.: Enhancing declare maps based on event correlations. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 97–112. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Maggi, F.M., Dumas, M., García-Bañuelos, L., Montali, M.: Discovering data-aware declarative process models from event logs. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 81–96. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Di Ciccio, C., Schouten, M.H.M., de Leoni, M., Mendling, J.: Declarative process discovery with minerful in prom. In: BPM Demos, pp. 60–64 (2015)Google Scholar
  22. 22.
    Bowman, J.S., Emerson, S.L., Darnovsky, M.: The Practical S.Q.L Handbook: Using Structured Query Language. Addison-Wesley Longman Publishing, Boston (1996)Google Scholar
  23. 23.
    Di Ciccio, C., Maggi, F.M., Montali, M., Mendling, J.: Ensuring model consistency in declarative process discovery. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) Business Process Management. LNCS, vol. 9253, pp. 144–159. Springer, Cham (2015)CrossRefGoogle Scholar
  24. 24.
    Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M., Edmond, D.: Workflow resource patterns: identification, representation and tool support. In: Pastor, Ó., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 216–232. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stefan Schönig
    • 1
  • Andreas Rogge-Solti
    • 1
  • Cristina Cabanillas
    • 1
  • Stefan Jablonski
    • 2
  • Jan Mendling
    • 1
  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.University of BayreuthBayreuthGermany

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