Skip to main content
SpringerLink
Log in

Towards a Rule-Based Recommendation Approach for Business Process Modeling

  • Conference paper
  • First Online:
Service-Oriented Computing – ICSOC 2020 Workshops (ICSOC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12632))

Included in the following conference series:

Abstract

Business process modeling can be time-consuming and error-prone, especially for inexperienced users. For this reason, graphical editors for business process modeling should support users by providing suggestions on how to complete a currently developed business process model. We address this problem with a rule-based activity recommendation approach, which suggests suitable activities to extend the business process model that is currently edited at a user-defined position. Contrary to alternative approaches, rules provide an additional explanation for the recommendation, which can be useful in cases where a user might be torn between two alternatives. We plan to investigate how rule learning can be efficiently designed for the given problem setting and how a rule-based approach performs compared to alternative methods. In this paper we describe the basic idea, a first implementation and first results.

Supervised by Heiner Stuckenschmidt (Data and Web Science Group, University of Mannheim, Mannheim, Germany), heiner@informatik.uni-mannheim.de.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    https://github.com/THUBPM/RLRecommender .

References

  1. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)

    Google Scholar 

  2. Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(8), 689–694 (1997)

    Article  Google Scholar 

  3. Cao, B., Yin, J., Deng, S., Wang, D., Wu, Z.: Graph-based workflow recommendation: on improving business process modeling. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1527–1531. Association for Computing Machinery, New York (2012)

    Google Scholar 

  4. Dataset consisting of processes collected from a district government in Hangzhou, China. https://github.com/THUBPM/RLRecommender/tree/master/dataset/SRD

  5. De Raedt, L.: Logical and Relational Learning. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-68856-3

  6. Dehaspe, L., Toivonen, H.: Discovery of relational association rules. In: Džeroski, S., LavračN. (eds.) Relational data mining, pp. 189–212. Springer, Berlin (2001). https://doi.org/10.1007/978-3-662-04599-2_8

  7. Deng, S., et al.: A recommendation system to facilitate business process modeling. IEEE Trans. Cybern. 47(6), 1380–1394 (2017)

    Article  Google Scholar 

  8. Fellmann, M., Zarvic, N., Metzger, D., Koschmider, A.: Requirements catalog for business process modeling recommender systems. In: Wirtschaftsinformatik, pp. 393–407 (2015)

    Google Scholar 

  9. Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422 (2013)

    Google Scholar 

  10. Jannach, D., Jugovac, M., Lerche, L.: Supporting the design of machine learning workflows with a recommendation system. ACM Trans. Interact. Intell. Syst. (TiiS) 6(1), 1–35 (2016)

    Article  Google Scholar 

  11. Kluza, K., Baran, M., Bobek, S., Nalepa, G.J.: Overview of recommendation techniques in business process modeling. In: KESE CEUR Workshop Proceedings, vol. 1070. CEUR-WS.org (2013)

    Google Scholar 

  12. Koschmider, A., Hornung, T., Oberweis, A.: Recommendation-based editor for business process modeling. Data Knowl. Eng. 70(6), 483–503 (2011)

    Article  Google Scholar 

  13. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  14. Li, Y., et al.: An efficient recommendation method for improving business process modeling. IEEE Trans. Ind. Inform. 10(1), 502–513 (2014)

    Article  Google Scholar 

  15. Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3137–3143. AAAI Press (2019)

    Google Scholar 

  16. Model collection of the Business Process Management Academic Initiative. http://bpmai.org/

  17. Wang, H., Wen, L., Lin, L., Wang, J.: RLRecommender: a representation-learning-based recommendation method for business process modeling. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 478–486. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_34

    Chapter  Google Scholar 

  18. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  19. Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. arXiv preprint arXiv:1804.11192 (2018)

Download references

Author information

Authors and Affiliations

  1. Intelligent Robotic Process Automation, SAP SE, Walldorf, Germany

    Diana Sola

  2. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Diana Sola

Authors
  1. Diana Sola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diana Sola .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Zayed University, Dubai, United Arab Emirates

    Fatma Outay

  3. University of New South Wales, Sydney, NSW, Australia

    Hye-young Paik

  4. Huawei Paris Research Center, Paris, France

    Amira Alloum

  5. National Research Council C.N.R., Pisa, Italy

    Marinella Petrocchi

  6. Deakin University, Waurn Ponds, VIC, Australia

    Mohamed Reda Bouadjenek

  7. Macquarie University, Sydney, NSW, Australia

    Amin Beheshti

  8. Rochester Institute of Technology, Rochester, NY, USA

    Xumin Liu

  9. Université de Paris, Paris, France

    Abderrahmane Maaradji

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sola, D. (2021). Towards a Rule-Based Recommendation Approach for Business Process Modeling. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-76352-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76351-0

  • Online ISBN: 978-3-030-76352-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation