Skip to main content

Lights, Camera, Action! Business Process Movies for Online Process Discovery

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 202))

Included in the following conference series:

Abstract

Nowadays, organizational information systems are able to collect high volumes of data in event logs every day. Through process mining techniques, it is possible to extract information from such logs to support organizations in checking process conformance, detecting bottlenecks, and carrying on performance analysis. However, to analyze such “big data” through process mining, events coming from process executions (in the form of event streams) must be processed on-the-fly as they occur. The work presented in this paper is built on top of a technique for the online discovery of declarative process models presented in our previous work. In particular, we introduce a tool providing a dynamic visualization of the models discovered over time showing, as a “process movie”, the sequence of valid business rules at any point in time based on the information retrieved from an event stream. The effectiveness of the visualizer is validated through an event stream pertaining to health insurance claims handling in a travel agency.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See http://www.processmining.org for more information.

  2. 2.

    The entire process movie generated in our experiment can be found at http://youtu.be/9gbrhkSfRTc.

  3. 3.

    See http://cpntools.org.

References

  1. van der Aalst, W.: Process mining: overview and opportunities. ACM Trans. Manage. Inf. Syst. 3(2), 7:1–7:17 (2012)

    Google Scholar 

  2. van der Aalst, W., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci. R&D 23, 99–113 (2009)

    Google Scholar 

  3. Aggarwal, C.: Data Streams: Models and Algorithms, Advances in Database Systems, vol. 31. Springer, Boston (2007)

    Book  Google Scholar 

  4. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis learning examples. J. Mach. Learn. Res. 11, 1601–1604 (2010)

    Google Scholar 

  5. Bose, R.J.C.: Process Mining in the Large: Preprocessing, Discovery, and Diagnostics. Ph.D. thesis, Eindhoven University of Technology (2012)

    Google Scholar 

  6. Burattin, A., Maggi, F., van der Aalst, W., Sperduti, A.: Techniques for a posteriori analysis of declarative processes. In: EDOC. pp. 41–50 (2012)

    Google Scholar 

  7. Burattin, A., Sperduti, A., van der Aalst, W.: Heuristics Miners for Streaming Event Data. CoRR abs/1212.6383 (2012)

    Google Scholar 

  8. Burattin, A., Sperduti, A., van der Aalst, W.: Control-flow discovery from event streams. In: Proceedings of the IEEE Congress on Evolutionary Computation. IEEE (2014). (to appear)

    Google Scholar 

  9. Da San Martino, G., Navarin, N., Sperduti, A.: A lossy counting based approach for learning on streams of graphs on a budget. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1294–1301. AAAI Press (2012)

    Google Scholar 

  10. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM SIGMOD Rec. 34(2), 18–26 (2005)

    Article  Google Scholar 

  11. Gama, J.A.: Knowledge Discovery from Data Streams, Chapman and Hall/CRC Data Mining and Knowledge Discovery Series, vol. 20103856. Chapman and Hall/CRC, Boca Raton (2010)

    Google Scholar 

  12. Golab, L., Özsu, M.T.: Issues in data stream management. ACM SIGMOD Rec. 32(2), 5–14 (2003)

    Article  Google Scholar 

  13. Günther, C., Verbeek, H.: XES Standard Definition (2009). http://www.xes-standard.org/

  14. Kupferman, O., Vardi, M.: Vacuity detection in temporal model checking. Int. J. Softw. Tools Technol. Transfer 4, 224–233 (2003)

    Article  Google Scholar 

  15. Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A.: Online process discovery to detect concept drifts in LTL-based declarative process models. In: Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., De Leenheer, P., Dou, D. (eds.) ODBASE 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: VLDB, pp. 346–357 (2002)

    Google Scholar 

  17. Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographies. ACM Trans. Web 4(1), 1–62 (2010)

    Article  Google Scholar 

  18. Pesic, M.: Constraint-Based Workflow Management Systems: Shifting Controls to Users. Ph.D. thesis, Beta Research School for Operations Management and Logistics, Eindhoven (2008)

    Google Scholar 

  19. Verbeek, E., Buijs, J., van Dongen, B., van der Aalst, W.: Prom 6: the process mining toolkit. In: Demo at the 8th International Conference on Business Process Management (BPM 2010) (2010)

    Google Scholar 

  20. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)

    Google Scholar 

Download references

Acknowledgment

The work of Andrea Burattin is supported by the Eurostars-Eureka project PROMPT (E!6696).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Burattin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Burattin, A., Cimitile, M., Maggi, F.M. (2015). Lights, Camera, Action! Business Process Movies for Online Process Discovery. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15895-2_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15894-5

  • Online ISBN: 978-3-319-15895-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics