Skip to main content
SpringerLink
Log in
Book cover

International Workshop on New Frontiers in Mining Complex Patterns

NFMCP 2013: New Frontiers in Mining Complex Patterns pp 67–81Cite as

Process Mining to Forecast the Future of Running Cases

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8399))

Abstract

Processes are everywhere in our daily lives. More and more information about executions of processes are recorded in event logs by several information systems. Process mining techniques are used to analyze historic information hidden in event logs and to provide surprising insights for managers, system developers, auditors, and end users. While existing process mining techniques mainly analyze full process instances (cases), this paper extends the analysis to running cases, which have not yet completed. For running cases, process mining can be used to notify future events. This forecasting ability can provide insights for check conformance and support decision making. This paper details a process mining approach, which uses predictive clustering to equip an execution scenario with a prediction model. This model accounts for recent events of running cases to predict the characteristics of future events. Several tests with benchmark logs investigate the viability of the proposed approach.

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   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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.xes-standard.org/

  2. 2.

    http://dtai.cs.kuleuven.be/clus/

  3. 3.

    http://www.processmining.org/event_logs_and_models_used_in_book

References

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

    Google Scholar 

  2. Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: ICML 1998, pp. 55–63. Morgan Kaufmann (1998)

    Google Scholar 

  3. Buffett, S., Geng, L.: Using classification methods to label tasks in process mining. J. Softw. Maint. Evol. 22(67), 497–517 (2010)

    Article  Google Scholar 

  4. van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: when will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)

    Google Scholar 

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

    Article  Google Scholar 

  6. Goedertier, S., Martens, D., Baesens, B., Haesen, R., Vanthienen, J.: Process mining as first-order classification learning on logs with negative events. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 42–53. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kauffmann, San Mateo (1993)

    Google Scholar 

  8. Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting flexible processes through recommendations based on history. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980, vol. 898, pp. 366–381. Springer, Heidelberg (1981)

    Chapter  Google Scholar 

  10. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    Book  Google Scholar 

  12. van der Aalst, W.M.P., Pesic, M., Song, M.: Beyond process mining: from the past to present and future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)

    Article  Google Scholar 

  14. 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)

    Chapter  Google Scholar 

  15. Žliobaitė, I.: Combining time and space similarity for small size learning under concept drift. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 412–421. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

Download references

Acknowledgments

This work fulfills the research objectives of the PON 02_00563_3470993 project “VINCENTE - A Virtual collective INtelligenCe ENvironment to develop sustainable Technology Entrepreneurship ecosystems” funded by the Italian Ministry of University and Research (MIUR). The authors wish to thank Gianluca Giorgio and Luca Nardulli for their support in developing the framework, anonymous reviewers of the workshop paper for their useful suggestions to improve the manuscript.

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, via Orabona, 4, 70126, Bari, Italy

    Sonja Pravilovic, Annalisa Appice & Donato Malerba

  2. Montenegro Business School, Mediterranean University, Vaka Djurovica b.b., Podgorica, Montenegro

    Sonja Pravilovic

Authors
  1. Sonja Pravilovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Annalisa Appice
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donato Malerba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annalisa Appice .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR, CNR, Rende, Italy

    Giuseppe Manco

  5. Rende, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Pravilovic, S., Appice, A., Malerba, D. (2014). Process Mining to Forecast the Future of Running Cases. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08407-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08406-0

  • Online ISBN: 978-3-319-08407-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation