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Encyclopedia of Big Data Technologies pp 1–8Cite as

Streaming Process Discovery and Conformance Checking

Synonyms

Online conformance checking; Online process discovery; Online process mining

Definitions

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.

Overview

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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Notes

  1. 1.

    http://plg.processmining.it/

  2. 2.

    http://www.cpntools.org/

  3. 3.

    http://www.promtools.org/

References

  • Aggarwal CC (2007) Data streams: models and algorithms. Advances in database systems. Springer, Boston. https://doi.org/10.1007/978-0-387-47534-9

  • 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

  • Bifet A, Kirkby R (2009) Data stream mining: a practical approach. Technical report, Centre for open software innovation – The University of Waikato

    Google Scholar 

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

    Google Scholar 

  • 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 1–6

    Google Scholar 

  • Burattin A (2017) Online conformance checking for petri nets and event streams. In: CEUR Workshop Proceedings, vol 1920

    Google Scholar 

  • 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). Springer

    Google Scholar 

  • Burattin A, Sperduti A, van der Aalst WM (2012) Heuristics miners for streaming event data. ArXiv CoRR http://arxiv.org/abs/1212.6383

  • 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 

  • 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

  • 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

  • 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 1294–13010

    Google Scholar 

  • Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. Springer

    Google Scholar 

  • Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. ACM Sigmod Rec 34(2):18–26. https://doi.org/10.1.1.80.798

  • Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRC, Boca Raton. https://doi.org/10.1201/EBK1439826119

  • Golab L, Özsu MT (2003) Issues in data stream management. ACM SIGMOD Rec 32(2):5–14. https://doi.org/10.1145/776985.776986

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 346–357

    Google Scholar 

  • 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

  • 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

  • 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

  • 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 91–107

    Google Scholar 

  • van der Aalst WM, Weijters TAJMM (2003) Rediscovering workflow models from event-based data using little thumb. Integr Comput Aided Eng 10(2):151–162

    Google Scholar 

  • van der Aalst WM, Weijters TAJMM, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16:2004

    Google Scholar 

  • 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

  • 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 85–89

    Google Scholar 

  • 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 210–228

    Google Scholar 

  • 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

  • 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

  • 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 45–49

    Google Scholar 

  • 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 

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Author information

Authors and Affiliations

  1. DTU Compute, Software Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark

    Andrea Burattin

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  1. Andrea Burattin
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Correspondence to Andrea Burattin .

Editor information

Editors and Affiliations

  1. School of Comp. Sci. and Engineering, University of New South Wales School of Comp. Sci. and Engineering, Eveleigh, New South Wales, Australia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. No affiliation provided

    Marlon Dumas

  2. No affiliation provided

    Matthias Weidlich

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Burattin, A. (2018). Streaming Process Discovery and Conformance Checking. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_103-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_103-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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Chapter history

  1. Latest

    Streaming Process Discovery and Conformance Checking
    Published:
    10 March 2022

    DOI: https://doi.org/10.1007/978-3-319-63962-8_103-2

  2. Original

    Streaming Process Discovery and Conformance Checking
    Published:
    12 March 2018

    DOI: https://doi.org/10.1007/978-3-319-63962-8_103-1

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