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Recurrent Process Mining with Live Event Data

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Business Process Management Workshops (BPM 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 308))

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Abstract

In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.

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Notes

  1. 1.

    denotes a powerset of sequences, i.e. \(L \subseteq E^*\).

  2. 2.

    See http://www.processmining.org and http://www.promtools.org.

  3. 3.

    https://svn.win.tue.nl/repos/prom/Packages/DatabaseInductiveMiner/Trunk/.

References

  1. Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S.: Ontology-based data access for extracting event logs from legacy data: the onprom tool and methodology. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 220–236. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_16

    Chapter  Google Scholar 

  2. Calvanese, D., Montali, M., Syamsiyah, A., van der Aalst, W.M.P.: Ontology-driven extraction of event logs from relational databases. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 140–153. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_12

    Chapter  Google Scholar 

  3. Di Ciccio, C., Mecella, M.: Mining constraints for artful processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 11–23. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30359-3_2

    Chapter  Google Scholar 

  4. Günther, C.W.: XES Standard Definition (2014). www.xes-standard.org

  5. Jans, M.J., Alles, M., Vasarhelyi, M.A.: Process Mining of Event Logs in Auditing: Opportunities and Challenges (2010). SSRN 2488737

    Google Scholar 

  6. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17

    Chapter  Google Scholar 

  7. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6

    Chapter  Google Scholar 

  8. 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.) OTM 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41030-7_7

    Chapter  Google Scholar 

  9. Mannhardt, F.: XESLite managing large XES event logs in ProM. BPM Center Report BPM-16-04 (2016)

    Google Scholar 

  10. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)

    Article  Google Scholar 

  11. Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_18

    Google Scholar 

  12. Suriadi, S., Wynn, M.T., Ouyang, C., ter Hofstede, A.H.M., van Dijk, N.J.: Understanding process behaviours in a large insurance company in australia: a case study. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 449–464. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_29

    Chapter  Google Scholar 

  13. Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P.: DB-XES: enabling process mining in the large. In: SIMPDA 2016, pp. 63–77 (2016)

    Google Scholar 

  14. Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Discovering social networks instantly: moving process mining computations to the database and data entry time. In: Reinhartz-Berger, I., Gulden, J., Nurcan, S., Guédria, W., Bera, P. (eds.) BPMDS/EMMSAD -2017. LNBIP, vol. 287, pp. 51–67. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59466-8_4

    Chapter  Google Scholar 

  15. Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Recurrent process mining on procedural and declarative approaches. BPM Center Report BPM-17-03 (2017)

    Google Scholar 

  16. van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016)

    Book  Google Scholar 

  17. van Dongen, B.F.: BPI Challenge 2017 (2017)

    Google Scholar 

  18. van Dongen, B.F., Shabani, S.: Relational XES: data management for process mining. In: CAiSE 2015, pp. 169–176 (2015)

    Google Scholar 

  19. van Zelst, S.J., Burattin, A., van Dongen, B.F., Verbeek, H.M.W.: Data streams in ProM 6: a single-node architecture. In: BPM Demo Session 2014, p. 81 (2014)

    Google Scholar 

  20. van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Know what you stream: generating event streams from CPN models in ProM 6. In: BPM Demo Session 2015, pp. 85–89 (2015)

    Google Scholar 

  21. van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Online discovery of cooperative structures in business processes. In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 210–228. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_12

    Chapter  Google Scholar 

  22. 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). https://doi.org/10.1007/978-3-642-17722-4_5

    Chapter  Google Scholar 

  23. Zhou, Z., Wang, Y., Li, L.: Process mining based modeling and analysis of workflows in clinical care - a case study in a Chicago Outpatient Clinic. In: ICNSC 2014, pp. 590–595 (2014)

    Google Scholar 

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Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Alifah Syamsiyah, Boudewijn F. van Dongen & Wil M. P. van der Aalst

Authors
  1. Alifah Syamsiyah
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  2. Boudewijn F. van Dongen
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  3. Wil M. P. van der Aalst
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Correspondence to Alifah Syamsiyah .

Editor information

Editors and Affiliations

  1. Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain

    Ernest Teniente

  2. Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Matthias Weidlich

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Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P. (2018). Recurrent Process Mining with Live Event Data. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-74030-0_13

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  • Online ISBN: 978-3-319-74030-0

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