Skip to main content
Springer Nature Link
Log in

A Time-Series Similarity Measure for Case-Based Deviation Management to Support Flexible Workflow Execution

  • Conference paper
  • First Online:
Case-Based Reasoning Research and Development (ICCBR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12311))

Included in the following conference series:

  • 1180 Accesses

Abstract

Our objective is to develop an approach based on case-based reasoning that detects and handles unforeseen deviations that occur in flexible workflow execution. With a case-based approach we aim at supporting the continuation of a deviant workflow execution by utilizing successfully completed processes, where similar deviations emerged. As a first step, this work introduces a novel similarity measure based on time sequence similarity that is able to compare running and completed workflow instances. We implemented and evaluated our approach in the ProCAKE framework. The proposed similarity measure achieves promising results considering runtime and similarity assessment.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    An implementation is included in the ProCAKE framework V1.2 and publicly available under http://procake.uni-trier.de.

  2. 2.

    Note that, in contrast to our approach, A* additionally searches for mappings between the two graphs’ edges which accounts for approximately half of the runtimes. Furthermore, due to resource restrictions, the search queue is limited to 10000 entries which may hinder the algorithm from finding the overall best mapping.

  3. 3.

    Retrieval time of the A* approach was corrected for edge mapping which accounts for approximately halve of the runtime. The experiments were conducted on a personal computer with an 8-core Intel Core i7-6700 CPU @3.4 GHz and 32 GB of RAM.

References

  1. van der Aalst, W.M.P.: Business process management - a comprehensive survey. ISRN Softw. Eng. 2013, 1–37 (2013)

    Article  Google Scholar 

  2. Adams, M., ter Hofstede, A.H.M., van der Aalst, W.M.P., Edmond, D.: Dynamic, extensible and context-aware exception handling for workflows. In: On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS, OTM Confederated International Conferences, Vilamoura, Portugal, 25–30 November 2007, Proceedings, Part I, pp. 95–112 (2007)

    Google Scholar 

  3. Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic workflows. Inf. Syst. 40, 115–127 (2014)

    Article  Google Scholar 

  4. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359–370 (1994)

    Google Scholar 

  5. Casati, F., Ceri, S., Paraboschi, S., Pozzi, G.: Specification and implementation of exceptions in workflow management systems. ACM Trans. Database Syst. 24(3), 405–451 (1999)

    Article  Google Scholar 

  6. Dijkman, R., Dumas, M., Van Dongen, B., Krik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)

    Article  Google Scholar 

  7. Döhring, M., Zimmermann, B., Godehardt, E.: Extended workflow flexibility using rule-based adaptation patterns with eventing semantics. In: Informatik 2010: Service Science - Neue Perspektiven für die Informatik, Beiträge der 40. Jahrestagung der GI, Band 1, Leipzig, Deutschland, pp. 195–200 (2010)

    Google Scholar 

  8. Grambow, G., Oberhauser, R., Reichert, M.: Event-driven exception handling for software engineering processes. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 414–426. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_40

    Chapter  Google Scholar 

  9. Grumbach, L., Bergmann, R.: Semaflex: a novel approach for implementing workflow flexibility by deviation based on constraint satisfaction problem solving. Expert Syst. (2019)

    Google Scholar 

  10. Grumbach, L., Bergmann, R.: Towards case-based deviation management for flexible workflows. In: Proceedings of the Conference LWDA, Berlin, Germany, 30 September–2 October 2019, pp. 241–252 (2019)

    Google Scholar 

  11. Grumbach, L., Rietzke, E., Schwinn, M., Bergmann, R., Kuhn, N.: SEMAFLEX - semantic integration of flexible workflow and document management. In: Proceedings of the Conference LWDA, Potsdam, Germany, 12–14 September 2016, pp. 43–50 (2016)

    Google Scholar 

  12. Grumbach, L., Rietzke, E., Schwinn, M., Bergmann, R., Kuhn, N.: SEMANAS - semantic support for grant application processes. In: Proceedings of the Conference LWDA, Mannheim, Germany, 22–24 August 2018, pp. 126–131 (2018)

    Google Scholar 

  13. Gundersen, O.E.: Toward measuring the similarity of complex event sequences in real-time. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 107–121. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32986-9_10

    Chapter  Google Scholar 

  14. Mueen, A., Keogh, E.: Extracting optimal performance from dynamic time warping. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2129–2130 (2016)

    Google Scholar 

  15. Müller, R., Greiner, U., Rahm, E.: A\({}_{\text{ gent }}\)w\({}_{\text{ ork }}\): a workflow system supporting rule-based workflow adaptation. Data Knowl. Eng. 51(2), 223–256 (2004)

    Article  Google Scholar 

  16. Obweger, H., Suntinger, M., Schiefer, J., Raidl, G.: Similarity searching in sequences of complex events. In: 2010 4th International Conference on Research Challenges in Information Science - Proceedings, RCIS 2010, pp. 631–640 (2010)

    Google Scholar 

  17. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Sig. Process. 26(1), 43–49 (1978)

    Article  Google Scholar 

  18. Schonenberg, H., Mans, R., Russell, N., Mulyar, N., van der Aalst, W.M.P.: Towards a taxonomy of process flexibility. In: Proceedings of the Forum at the CAiSE 2008 Conference, Montpellier, France, 18–20 June 2008, pp. 81–84 (2008)

    Google Scholar 

  19. da Silva, M.A.A., Bendraou, R., Robin, J., Blanc, X.: Flexible deviation handling during software process enactment. In: Workshops Proceedings of the 15th IEEE International Enterprise Distributed Object Computing Conference, EDOCW 2011, Helsinki, Finland, 29 August–2 September 2011, pp. 34–41 (2011)

    Google Scholar 

  20. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)

    Article  Google Scholar 

  21. Weber, B., Wild, W., Breu, R.: CBRFlow: enabling adaptive workflow management through conversational case-based reasoning. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 434–448. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_32

    Chapter  Google Scholar 

  22. Wombacher, A., Rozie, M.: Evaluation of workflow similarity measures in service discovery. In: Service-Oriented Electronic Commerce, Proceedings zur Konferenz im Rahmen der MKWI 2006. Gesellschaft für Informatik eV (2006)

    Google Scholar 

  23. Zarka, R., Cordier, A., Egyed-Zsigmond, E., Lamontagne, L., Mille, A.: Similarity measures to compare episodes in modeled traces. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS (LNAI), vol. 7969, pp. 358–372. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39056-2_26

    Chapter  Google Scholar 

Download references

Acknowledgements

This work is part of the research project SEMANAS and is funded by the Federal Ministry of Education and Research (BMBF), grant no. 13FH013IX6.

Author information

Authors and Affiliations

  1. Business Information Systems II, University of Trier, 54296, Trier, Germany

    Erik Schake, Lisa Grumbach & Ralph Bergmann

  2. Trier University of Applied Sciences, 55761, Birkenfeld, Germany

    Lisa Grumbach

  3. German Research Center for Artificial Intelligence (DFKI), Branch University of Trier, Behringstraße 21, 54296, Trier, Germany

    Ralph Bergmann

Authors
  1. Erik Schake
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisa Grumbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ralph Bergmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lisa Grumbach .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Auckland, Auckland, New Zealand

    Ian Watson

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, USA

    Rosina Weber

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

Schake, E., Grumbach, L., Bergmann, R. (2020). A Time-Series Similarity Measure for Case-Based Deviation Management to Support Flexible Workflow Execution. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58342-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58341-5

  • Online ISBN: 978-3-030-58342-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics