Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Process Management

BPM 2020: Business Process Management pp 3–16Cite as

Process Minding: Closing the Big Data Gap

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12168))

Abstract

The discipline of process mining was inaugurated in the BPM community. It flourished in a world of small(er) data, with roots in the communities of software engineering and databases and applications mainly in organizational and management settings. The introduction of big data, with its volume, velocity, variety, and veracity, and the big strides in data science research and practice pose new challenges to this research field. The paper positions process mining along modern data life cycle, highlighting the challenges and suggesting directions in which data science disciplines (e.g., machine learning) may interact with a renewed process mining agenda.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.win.tue.nl/bpi/doku.php?id=2011:challenge.

  2. 2.

    https://tinyurl.com/yadduec4, https://tinyurl.com/ybtxrl53.

  3. 3.

    https://tinyurl.com/y9l6njyh.

References

  1. van der Aalst, W.M.P.: Process Mining: Discovery. Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3

  2. Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance Checking. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-99414-7

    Book  Google Scholar 

  3. Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31

    Chapter  Google Scholar 

  4. Dumas, M., Van der Aalst, W.M., Ter Hofstede, H.: Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, Hoboken (2005)

    Book  Google Scholar 

  5. Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215. IEEE (2018)

    Google Scholar 

  6. Senderovich, A., et al.: Data-driven performance analysis of scheduled processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 35–52. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_3

    Chapter  Google Scholar 

  7. Gal, A., Mandelbaum, A., Schnitzler, F., Senderovich, A., Weidlich, M.: Traveling time prediction in scheduled transportation with journey segments. Inf. Syst. 64, 266–280 (2017)

    Article  Google Scholar 

  8. Maisenbacher, M., Weidlich, M.: Handling concept drift in predictive process monitoring. In: 2017 IEEE International Conference on Services Computing (SCC), pp. 1–8. IEEE (2017)

    Google Scholar 

  9. Bose, R.J.C., Van Der Aalst, W.M., Žliobaitė, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2013)

    Article  Google Scholar 

  10. Spenrath, Y., Hassani, M.: Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In: EDBT/ICDT Workshops (2019)

    Google Scholar 

  11. van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Event stream-based process discovery using abstract representations. Knowl. Inf. Syst. 54(2), 407–435 (2017). https://doi.org/10.1007/s10115-017-1060-2

    Article  Google Scholar 

  12. Senderovich, A.: Queue mining: service perspectives in process mining. Ph.D. dissertation, Technion-Israel Institute of Technology (2017)

    Google Scholar 

  13. Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining – predicting delays in service processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 42–57. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_4

    Chapter  Google Scholar 

  14. van Dongen, B.F., Adriansyah, A.: Process mining: fuzzy clustering and performance visualization. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 158–169. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_15

    Chapter  Google Scholar 

  15. Senderovich, A., Shleyfman, A., Weidlich, M., Gal, A., Mandelbaum, A.: To aggregate or to eliminate? Optimal model simplification for improved process performance prediction. Inf. Syst. 78, 96–111 (2018)

    Article  Google Scholar 

  16. Van Der Aalst, W.: Data science in action. In: van der Aalst, W. (ed.) Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1

    Chapter  Google Scholar 

  17. Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31(4), 686–705 (2018)

    Article  MathSciNet  Google Scholar 

  18. Vom Brocke, J., Rosemann, M.: Handbook on Business Process Management 1: Introduction, Methods, and Information Systems. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45100-3

    Book  Google Scholar 

  19. Lu, X., et al.: Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 154–174. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_11

    Chapter  Google Scholar 

  20. Senderovich, A., Rogge-Solti, A., Gal, A., Mendling, J., Mandelbaum, A.: The ROAD from sensor data to process instances via interaction mining. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 257–273. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_16

    Chapter  Google Scholar 

  21. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_8

    Chapter  Google Scholar 

  22. Günther, C.W., van der Aalst, W.M.: Mining activity clusters from low-level event logs. Beta, Research School for Operations Management and Logistics (2006)

    Google Scholar 

  23. De San Pedro, J., Carmona, J., Cortadella, J.: Log-based simplification of process models. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 457–474. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_30

    Chapter  Google Scholar 

  24. Shraga, R., Gal, A., Schumacher, D., Senderovich, A., Weidlich, M.: Process discovery with context-aware process trees. Inf. Syst. 101533 (2020)

    Google Scholar 

  25. Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2(2), 182–192 (2012)

    Google Scholar 

  26. Sagi, T., Gal, A.: Non-binary evaluation measures for big data integration. VLDB J. 27(1), 105–126 (2017). https://doi.org/10.1007/s00778-017-0489-y

    Article  Google Scholar 

  27. Lin, D., et al.: An information-theoretic definition of similarity. Icml 98, 296–304 (1998)

    Google Scholar 

  28. Rogge-Solti, A., Senderovich, A., Weidlich, M., Mendling, J., Gal, A.: In log and model we trust? A generalized conformance checking framework. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 179–196. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_11

    Chapter  Google Scholar 

  29. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  30. Senderovich, A., Beck, J.C., Gal, A., Weidlich, M.: Congestion graphs for automated time predictions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4854–4861 (2019)

    Google Scholar 

  31. Pan, F., Converse, T., Ahn, D., Salvetti, F., Donato, G.: Feature selection for ranking using boosted trees. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2025–2028 (2009)

    Google Scholar 

Download references

Acknowledgement

We thank Matthias Weidlich and Roee Shraga for fruitful discussions.

Author information

Authors and Affiliations

  1. Technion – Israel Institute of Technology, Technion City, 82000, Haifa, Israel

    Avigdor Gal

  2. University of Toronto, Toronto, Canada

    Arik Senderovich

Authors
  1. Avigdor Gal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arik Senderovich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avigdor Gal .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Dirk Fahland

  2. FBK-irst, Trento, Italy

    Chiara Ghidini

  3. University of Münster, Münster, Germany

    Jörg Becker

  4. University of Tartu, Tartu, Estonia

    Marlon Dumas

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gal, A., Senderovich, A. (2020). Process Minding: Closing the Big Data Gap. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58666-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58665-2

  • Online ISBN: 978-3-030-58666-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation