Skip to main content
SpringerLink
Log in

Adequate Basis for the Data-Driven and Machine-Learning-Based Identification

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14125))

Included in the following conference series:

  • 282 Accesses

Abstract

Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

References

  1. Peffers, K., et al.: The design science research process: a model for producing and presenting information systems research. In: 1st International Conference on Design Science in Information Systems and Technology (DESRIST), vol. 24, no. 3, pp. 83–106 (2006)

    Google Scholar 

  2. van der Aalst, W.M.P.: Data science in action. in Process Mining. Springer, Berlin, Heidelberg, pp. 3–23 (2016). https://doi.org/10.1007/978-3-662-49851-4

  3. van der Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Software Engineering, pp. 1–37 (2013). https://doi.org/10.1155/2013/507984

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

  5. van der Aalst, W.M.P.: Data Scientist: The Engineer of the Future. In: Mertins, K., Benaben, F., Poler, R., Bourrieres, J., editors, Proceedings of the I-ESA Conference, volume 7 of Enterprise Interoperability, pp. 13–28. Springer-Verlag, Berlin (2014). https://doi.org/10.1007/978-3-319-04948-9_2

  6. Aalst, W.M.P.: Extracting event data from databases to unleash process mining. In: vom Brocke, J., Schmiedel, T. (eds.) BPM - Driving Innovation in a Digital World. MP, pp. 105–128. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14430-6_8

    Chapter  Google Scholar 

  7. Grum, M.: Managing human and artificial knowledge bearers. In: Shishkov, B. (ed.) BMSD 2020. LNBIP, vol. 391, pp. 182–201. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52306-0_12

    Chapter  Google Scholar 

  8. Gronau, N.: Modeling and analyzing knowledge intensive business processes with KMDL: comprehensive insights into theory and practice, p. 7. GITOmbh, Berlin (2012)

    Google Scholar 

  9. Allesandro, B., van der Aalst, W.M.P.: A novel token-based replay technique to speed up conformance checking and process enhancement (2020)

    Google Scholar 

  10. Zwicky, F.: Discovery, Invention, Research - Through the Morphological Approach. The Macmillan Company, Toronto (1969)

    Google Scholar 

  11. Ritchey, T.: Problem structuring using computer-aided morphological analysis. J. Oper. Res. Soc., Special Issue Probl. Struct. Methods 57(7), 792–801 (2006)

    MATH  Google Scholar 

  12. Zwicky, F.: Entdecken, Erfinden, Forschen im morphologischen Weltbild. D. Knaur, California (1966)

    Google Scholar 

  13. van Dongen, B.F., Alves de Medeiros, A.K., Wen, L.: Process mining: overview and outlook of petri net discovery algorithms. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 225–242. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00899-3_13

    Chapter  Google Scholar 

  14. van der Aalst, W.M.P., Weijters, A.J.M.M., Eds.: Process Mining, Special Issue of Computers in Industry, vol. 53(3). Elsevier Science Publishers, Amsterdam (2004)

    Google Scholar 

  15. can der Aalst, W.M.P., Rubin, V., can Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach using transition systems and regions. BPM Center Report BPM-06-30, BPMcenter.org (2006)

    Google Scholar 

  16. Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Mining Knowl. Discov. 14(2), 245–304 (2007)

    Article  MathSciNet  Google Scholar 

  17. M. Grum, D. Kotarski, M. Ambros, T. Biru, H. Krallmann, N. Gronau. "Managing Knowledge of Intelligent Systems - The Design of a Chatbot Using Domain-Specific Knowledge". in: Business Modeling and Software Design. Springer International Publishing, 2021

    Google Scholar 

  18. McKinney, S.M., et al.: International evaluation of an AI system for breast cancer screening. In: Nature, vol. 577, issue 7788, p. 89 (2020). https://doi.org/10.1038/s41586-019-1799-6

  19. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. In: Computers in Biology and Medicine, vol. 121, Article Number 103792 (2020). https://doi.org/10.1016/j.compbiomed.2020.103792

  20. Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015). https://doi.org/10.1016/j.rser.2015.04.065

    Article  Google Scholar 

  21. Zhao, Z., Chen, W.H., Wu, X.M., Chen, P.C.Y., Liu, J.M.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transport Syst. 11(2), 68–75 (2017). https://doi.org/10.1049/iet-its.2016.0208

    Article  Google Scholar 

  22. Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aid. Eng. 10(2), 151–162 (2003)

    Article  Google Scholar 

  23. Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining - adaptive process simplication based on multi-perspective metrics. In: Alonso, G., et al., Eds., BPM, vol. 4714 of Lecture Notes in Computer Science, pp. 328–343. Springer (2007). https://doi.org/10.1007/978-3-540-75183-0_24

  24. Color, J., Desel. J.: Application and theory of petri nets and concurrency. In: 34th International Conference, PETRI NETS 2013 Milan, Italy, Proceedings, pp. 311–329 (2013)

    Google Scholar 

  25. Pourmirza, S., Dijkman, R., Grefen, P.: Correlation mining: mining process orchestratoins without case identifiers. In: Eindhoven University of Technology, The Netherlands, Springer-Verlag, Berlin Heidelberg, pp. 237–252 (2015)

    Google Scholar 

  26. van der Aalst, W.M.P., et al.: Business process mining: an industrial application. Inf. Syst. 32(5), 713–732 (2007). https://doi.org/10.1016/j.is.2006.05.003

    Article  Google Scholar 

  27. Weijters, A.J.M.M., van der Aalst, W.M.P., Alves de Medeiros, A.K.: Process mining with heurisitics miner algorithm. In: BETA Working Paper Series, WP 166, Eindhoven University of Technology, Eindhoven (2006)

    Google Scholar 

  28. van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  29. Alves de Medeiros, A.K.: Genetic process mining. PhD thesis, Eindhoven University of Technology, Eindhoven, The Netherlands (2006)

    Google Scholar 

  30. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support in applications and theory of petri nets 2005. Proceedings 3536, 444–454 (2005)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Potsdam, Potsdam, Germany

    Marcel Rojahn, Norbert Gronau & Marcus Grum

  2. Krallmann AG, Berlin, Germany

    Maximilian Ambros, Tibebu Biru & Hermann Krallmann

Authors
  1. Marcel Rojahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maximilian Ambros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tibebu Biru
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hermann Krallmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Norbert Gronau
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcus Grum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcel Rojahn .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rojahn, M., Ambros, M., Biru, T., Krallmann, H., Gronau, N., Grum, M. (2023). Adequate Basis for the Data-Driven and Machine-Learning-Based Identification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42505-9_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42504-2

  • Online ISBN: 978-3-031-42505-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation