Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Streaming Process Discovery and Conformance Checking

  • Andrea Burattin
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_103-1



Streaming process discovery, streaming conformance checking, and streaming process mining in general (also known as online process mining) are disciplines which analyze event streams to extract a process model or to assess their conformance with respect to a given reference model. The main characteristic of this family of techniques is to analyze events immediately as they are generated (instead of storing them in a log for late processing). This allows to drastically reduce the latency among phases of the BPM lifecycle (cf. Dumas et al. 2013), thus allowing faster process adaptations and better executions.


A possible characterization of process mining algorithms is based on how they consume event data. Specifically, most of the algorithms focus on a (static) event log; however, there are algorithms which focus on event streams. An event log is a finite sampling of activities...

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  1. Aggarwal CC (2007) Data streams: models and algorithms. Advances in database systems. Springer, Boston. https://doi.org/10.1007/978-0-387-47534-9
  2. Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, pp 1–16. https://doi.org/10.1145/543614.543615
  3. Bifet A, Kirkby R (2009) Data stream mining: a practical approach. Technical report, Centre for open software innovation – The University of WaikatoGoogle Scholar
  4. Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis learning examples. J Mach Learn Res 11:1601–1604Google Scholar
  5. Burattin A (2016) PLG2 : Multiperspective process randomization with online and offline simulations. In: Online proceedings of the BPM Demo Track 2016, CEUR-WS.org, vol 1789, pp 1–6Google Scholar
  6. Burattin A (2017) Online conformance checking for petri nets and event streams. In: CEUR Workshop Proceedings, vol 1920Google Scholar
  7. Burattin A, Carmona J (2017, in press) A framework for online conformance checking. In: Proceedings of the 13th international workshop on business process intelligence (BPI 2017). SpringerGoogle Scholar
  8. Burattin A, Sperduti A, van der Aalst WM (2012) Heuristics miners for streaming event data. ArXiv CoRR http://arxiv.org/abs/1212.6383
  9. Burattin A, Maggi FM, Cimitile M (2014a) Lights, camera, action! business process movies for online process discovery. In: Proceedings of the 3rd international workshop on theory and applications of process visualization (TAProViz 2014)Google Scholar
  10. Burattin A, Sperduti A, van der Aalst WM (2014b) Control-flow discovery from event streams. In: Proceedings of the IEEE congress on evolutionary computation. IEEE, pp 2420–2427.  https://doi.org/10.1109/CEC.2014.6900341
  11. Burattin A, Cimitile M, Maggi FM, Sperduti A (2015) Online discovery of declarative process models from event streams. IEEE Trans Serv Comput 8(6):833–846.  https://doi.org/10.1109/TSC.2015.2459703
  12. Da San Martino G, Navarin N, Sperduti A (2012) 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. AAAI Press, pp 1294–13010Google Scholar
  13. Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. SpringerGoogle Scholar
  14. Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. ACM Sigmod Rec 34(2):18–26. https://doi.org/
  15. Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRC, Boca Raton.  https://doi.org/10.1201/EBK1439826119
  16. Golab L, Özsu MT (2003) Issues in data stream management. ACM SIGMOD Rec 32(2):5–14. https://doi.org/10.1145/776985.776986
  17. Hassani M, Siccha S, Richter F, Seidl T (2015) Efficient process discovery from event streams using sequential pattern mining. In: 2015 IEEE symposium series on computational intelligence, pp 1366–1373.  https://doi.org/10.1109/SSCI.2015.195
  18. Karp RM, Shenker S, Papadimitriou CH (2003) A simple algorithm for finding frequent elements in streams and bags. ACM Trans Database Syst 28(1):51–55. https://doi.org/10.1145/762471.762473
  19. Leemans SJJ, Fahland D, van der Aalst WM (2013) Discovering block-structured process models from event logs – a constructive approach. In: Proceedings of Petri nets. Springer, Berlin/Heidelberg, pp 311–329. https://doi.org/10.1007/978-3-642-38697-8{_}17
  20. Maggi FM, Montali M, Westergaard M, van der Aalst WM (2011) Monitoring business constraints with linear temporal logic: an approach based on colored automata. In: Proceedings of the 9th international conference on business process management. Springer, Berlin/Heidelberg, pp 132–147. https://doi.org/10.1007/978-3-642-23059-2_13
  21. Maggi FM, Montali M, van der Aalst WM (2012) An operational decision support framework for monitoring business constraints. In: Proceedings of 15th international conference on fundamental approaches to software engineering (FASE), pp 146–162. https://doi.org/10.1007/978-3-642-28872-2_11
  22. Maggi FM, Bose RPJC, van der Aalst WM (2013) A knowledge-based integrated approach for discovering and repairing declare maps. In: 25th international conference, CAiSE 2013, 17–21 June 2013. Springer, Berlin/Heidelberg/Valencia, pp 433–448. https://doi.org/10.1007/978-3-642-38709-8_28
  23. Manku GS, Motwani R (2002) Approximate frequency counts over data streams. In: Proceedings of international conference on very large data bases. Morgan Kaufmann, Hong Kong, pp 346–357Google Scholar
  24. Metwally A, Agrawal D, Abbadi AE (2005) Efficient computation of frequent and Top-k elements in data streams. In: Database theory – ICDT 2005. Springer, Berlin/Heidelberg, pp 398–412. https://doi.org/10.1007/978-3-540-30570-5_27
  25. Pesic M, Schonenberg H, van der Aalst WM (2007) DECLARE: full support for loosely-structured processes. In: Proceedings of EDOC. IEEE, pp 287–298.  https://doi.org/10.1109/EDOC.2007.14
  26. Redlich D, Molka T, Gilani W, Blair G, Rashid A (2014a) Constructs competition miner: process control-flow discovery of BP-domain constructs. In: Proceedings of BPM 2014, pp 134–150. https://doi.org/10.1007/978-3-319-10172-9_9
  27. Redlich D, Molka T, Gilani W, Blair G, Rashid A (2014b) Scalable dynamic business process discovery with the constructs competition miner. In: Proceedings of the 4th international symposium on data-driven process discovery and analysis (SIMPDA 2014), vol 1293, pp 91–107Google Scholar
  28. van der Aalst WM, Weijters TAJMM (2003) Rediscovering workflow models from event-based data using little thumb. Integr Comput Aided Eng 10(2):151–162Google Scholar
  29. van der Aalst WM, Weijters TAJMM, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16:2004Google Scholar
  30. van der Aalst WM, Günther CW, Rubin V, Verbeek EHMW, Kindler E, van Dongen B (2008) Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model 9(1):87–111. https://doi.org/10.1007/s10270-008-0106-z
  31. van Zelst SJ, van Dongen B, van der Aalst WM (2015) Know What you stream: generating event streams from CPN models in ProM 6. In: CEUR workshop proceedings, pp 85–89Google Scholar
  32. van Zelst SJ, van Dongen B, van der Aalst WM (2016) Online discovery of cooperative structures in business processes. In: Proceedings of the OTM 2016 conferences. Springer, pp 210–228Google Scholar
  33. van Zelst SJ, Bolt A, Hassani M, van Dongen B, van der Aalst WM (2017a) Online conformance checking: relating event streams to process models using prefix-alignments. Int J Data Sci Analy. https://doi.org/10.1007/s41060-017-0078-6
  34. van Zelst SJ, van Dongen B, van der Aalst WM (2017b) Event stream-based process discovery using abstract representations. Knowl Inform Syst pp 1–29. https://doi.org/10.1007/s10115-017-1060-2
  35. Weber I, Rogge-Solti A, Li C, Mendling J (2015) CCaaS: online conformance checking as a service. In: Proceedings of the BPM demo session 2015, vol 1418, pp 45–49Google Scholar
  36. Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101. https://doi.org/10.1007/BF00116900 Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.DTU Compute, Software EngineeringTechnical University of Denmark2800 Kgs. LyngbyDenmark

Section editors and affiliations

  • Marlon Dumas
    • 1
  • Matthias Weidlich
  1. 1.Institute of Computer ScienceUniversity of TartuTartuEstonia