Skip to main content
Springer Nature Link
Log in

An SQL-Based Declarative Process Mining Framework for Analyzing Process Data Stored in Relational Databases

  • Conference paper
  • First Online:
Business Process Management Forum (BPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 490))

Included in the following conference series:

Abstract

Recently, the idea of applying process data analysis over relational databases (DBs) has been investigated in the process mining field resulting into different DB schemas that can be used to effectively store process data coming from Process-Aware Information Systems (PAISs). However, although SQL queries are particularly suitable to check declarative rules over traces stored in a DB, a deep analysis of how the existing instruments for SQL-based process mining can be effectively used for process analysis tasks based on declarative process modeling languages is still missing. In this paper, we present a full-fledged framework based on SQL queries over relational DBs for different declarative process mining use cases, i.e., process discovery, conformance checking, and query checking. The framework is used to benchmark different SQL-based solutions for declarative process mining, using synthetic and real-life event logs, with the aim of exploring their strengths and weaknesses.

The work of F. Riva was funded by the PRISMA project of the Free University of Bozen-Bolzano and by the Estonian Research Council (PRG1226). The work of D. Benvenuti and A. Marrella was supported by the PNRR MUR project PE0000013-FAIR, the H2020 project DataCloud (Grant number 101016835), and the Sapienza project DISPIPE.

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
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/francxx96/XEStoDB.

  2. 2.

    Here the support corresponds to the event support introduced in [7].

  3. 3.

    We set a timeout on the population scripts and each script that did not end within 24 h was stopped. Dashes in the tables mean that the corresponding scripts reached the timeout.

  4. 4.

    We set a timeout of 30 min on the query scripts.

References

  1. Aamer, H., Montali, M., Van den Bussche, J.: What Can Database Query Processing Do for Instance-Spanning Constraints? CoRR abs/2206.00140 (2022)

    Google Scholar 

  2. Alman, A., Di Ciccio, C., Haas, D., Maggi, F.M., Nolte, A.: Rule Mining with RuM. In: 2nd International Conference on Process Mining, ICPM 2020, Padua, Italy, 4–9 October 2020, pp. 121–128 (2020)

    Google Scholar 

  3. Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)

    Article  Google Scholar 

  4. Calì, A., Calvanese, D., Lenzerini, M.: Data integration under integrity constraints. In: Seminal Contributions to Information Systems Engineering, 25 Years of CAiSE, pp. 335–352. Springer (2013). https://doi.org/10.1007/978-3-642-36926-1

  5. Cecconi, A., De Giacomo, G., Di Ciccio, C., Maggi, F.M., Mendling, J.: Measuring the interestingness of temporal logic behavioral specifications in process mining. Inf. Syst. 107, 101920 (2022)

    Article  Google Scholar 

  6. Chiariello, F., Maggi, F.M., Patrizi, F.: ASP-Based Declarative Process Mining. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2022)

    Google Scholar 

  7. Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM Trans. Manag. Inf. Syst. 5(4), 24:1–24:37 (2015)

    Google Scholar 

  8. Donadello, I., Riva, F., Maggi, F.M., Shikhizada, A.: Declare4Py: a python library for declarative process mining. In: Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track at BPM 2022, pp. 117–121 (2022)

    Google Scholar 

  9. van Dongen, B.F.: BPI Challenge 2012 (Apr 2012)

    Google Scholar 

  10. van Dongen, B.F.: BPI Challenge 2017 (Feb 2017)

    Google Scholar 

  11. van Dongen, B.F.: International Declarations Log. BPI Challenge 2020 (Mar 2020)

    Google Scholar 

  12. van Dongen, B.F.: Travel Permits Log. BPI Challenge 2020 (Mar 2020)

    Google Scholar 

  13. van Dongen, B.F., Shabani, S.: Relational XES: data management for process mining. In: CAiSE Forum 2015, pp. 169–176 (2015)

    Google Scholar 

  14. Ewen, S., Kache, H., Markl, V., Raman, V.: Progressive query optimization for federated queries. In: Advances in Database Technology - EDBT 2006. vol. 3896, pp. 847–864 (2006)

    Google Scholar 

  15. Ghahfarokhi, A., Park, G., Berti, A., van der Aalst, W.M.P.: OCEL: A Standard for Object-Centric Event Logs, pp. 169–175 (07 2021)

    Google Scholar 

  16. Hildebrandt, T.T., Mukkamala, R.R., Slaats, T., Zanitti, F.: Contracts for cross-organizational workflows as timed Dynamic Condition Response Graphs. J. Log. Algebr. Program. 82(5–7), 164–185 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kulkarni, K.G., Michels, J.: Temporal features in SQL: 2011. SIGMOD Rec. 41(3), 34–43 (2012)

    Article  Google Scholar 

  18. de Leoni, M., Mannhardt, F.: Road Traffic Fine Management Process (Feb 2015)

    Google Scholar 

  19. Li, G., de Murillas, E.G.L., de Carvalho, R.M., van der Aalst, W.M.P.: Extracting object-centric event logs to support process mining on databases. In: Information Systems in the Big Data Era, pp. 182–199 (2018)

    Google Scholar 

  20. Maggi, F.M.: Discovering metric temporal business constraints from event logs. In: Johansson, B., Andersson, B., Holmberg, N. (eds.) BIR 2014. LNBIP, vol. 194, pp. 261–275. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11370-8_19

    Chapter  Google Scholar 

  21. Maggi, F.M.: Declarative Process Mining. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77525-8_92

  22. Mannhardt, F.: Sepsis Cases - Event Log (Dec 2016)

    Google Scholar 

  23. Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: IEEE International EDOC Conference 2007, pp. 287–300 (2007)

    Google Scholar 

  24. Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 287–300 (2007)

    Google Scholar 

  25. Polyvyanyy, A., ter Hofstede, A.H.M., La Rosa, M., Ouyang, C., Pika, A.: Process query language: Design, implementation, and evaluation. CoRR abs/1909.09543 (2019)

    Google Scholar 

  26. Räim, M., Di Ciccio, C., Maggi, F.M., Mecella, M., Mendling, J.: Log-based understanding of business processes through temporal logic query checking. In: Meersman, R. (ed.) OTM 2014. LNCS, vol. 8841, pp. 75–92. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45563-0_5

    Chapter  Google Scholar 

  27. Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and Customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_18

    Chapter  Google Scholar 

  28. Schönig, S.: SQL Queries for Declarative Process Mining on Event Logs of Relational Databases. CoRR abs/1512.00196 (2015)

    Google Scholar 

  29. Schönig, S., Di Ciccio, C., Maggi, F.M., Mendling, J.: Discovery of multi-perspective declarative process models. In: Sheng, Q.Z., Stroulia, E., Tata, S., Bhiri, S. (eds.) ICSOC 2016. LNCS, vol. 9936, pp. 87–103. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46295-0_6

    Chapter  Google Scholar 

  30. Schönig, S., Di Ciccio, C., Mendling, J.: Configuring SQL-based process mining for performance and storage optimisation. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 94–97. SAC 2019 (2019)

    Google Scholar 

  31. Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P.: DB-XES: enabling process discovery in the large. In: Ceravolo, P., Guetl, C., Rinderle-Ma, S. (eds.) SIMPDA 2016. LNBIP, vol. 307, pp. 53–77. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74161-1_4

    Chapter  Google Scholar 

  32. 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: Information Systems Evolution - CAiSE Forum. vol. 72, pp. 60–75 (2010)

    Google Scholar 

  33. Zeising, M., Schönig, S., Jablonski, S.: Towards a Common Platform for the Support of Routine and Agile Business Processes. In: Collaborative Computing: Networking, Applications and Worksharing (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Francesco Riva, Fabrizio Maria Maggi & Marco Montali

  2. University of Tartu, Tartu, Estonia

    Francesco Riva

  3. Datalane SRL, Verona, Italy

    Francesco Riva

  4. Sapienza University of Rome, Rome, Italy

    Dario Benvenuti & Andrea Marrella

Authors
  1. Francesco Riva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dario Benvenuti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabrizio Maria Maggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Marrella
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Montali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Riva .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Chiara Di Francescomarino

  2. Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin

  3. TU Dortmund University, Dortmund, Germany

    Christian Janiesch

  4. The University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

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

Riva, F., Benvenuti, D., Maggi, F.M., Marrella, A., Montali, M. (2023). An SQL-Based Declarative Process Mining Framework for Analyzing Process Data Stored in Relational Databases. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41623-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41622-4

  • Online ISBN: 978-3-031-41623-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics