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Optimized SAT encoding of conformance checking artefacts

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Abstract

Conformance checking is a growing discipline that aims at assisting organizations in monitoring their processes. On its core, conformance checking relies on the computation of particular artefacts which enable reasoning on the relation between observed and modeled behavior. It is widely acknowledge that the computation of these artifacts is the lion’s share of conformance checking techniques. This paper shows how important conformance artefacts like alignments, anti-alignments or multi-alignments, defined over the Levenshtein edit distance, can be efficiently computed by encoding the problem as an optimized SAT instance. From a general perspective, the work advocates for a unified family of techniques that can compute conformance artefacts in the same way. The implementation of the techniques presented in this paper show capabilities for dealing with both synthetic and real-life instances, which may open the door for a fresh way of applying conformance checking in the near future.

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Notes

  1. https://www.marketsandmarkets.com/Market-Reports/process-analytics-market-254139591.html.

  2. We use the logic operator \( A \Leftrightarrow B\) as a shortcut to \(A \Rightarrow B \wedge B \Rightarrow A\).

  3. SAT problems with minimization objectives and weigthed variables are also called MaxSAT problems.

  4. Anti-alignment Precision/Generalization of package AntiAlignments of ProM software version 6.8, http://www.promtools.org/.

  5. The da4py library and examples are available at https://github.com/BoltMaud/da4py.

  6. https://data.4tu.nl/repository/collection:event_logs_real.

References

  1. Adriansyah A (2014) Aligning observed and modeled behavior. PhD thesis, Department of Mathematics and Computer Science

  2. Augusto A, Conforti R, Dumas M, La Rosa M, Polyvyanyy A (2019) Split miner: automated discovery of accurate and simple business process models from event logs. Knowl Inf Syst 59(2):251–284

    Article  Google Scholar 

  3. Arturs B, Piotr I (2015) Edit distance cannot be computed in strongly subquadratic time (unless seth is false). In: Proceedings of the 47th annual ACM symposium on theory of computing. ACM, pp 51–58

  4. Bauer M, van der Aa H, Weidlich M (2019) Estimating process conformance by trace sampling and result approximation. In: Business process management- 17th international conference, BPM 2019, Vienna, Austria, 1–6 Sept 2019, Proceedings, pp 179–197

  5. Berti A, van Zelst Sebastiaan J, van der Aalst W (2019) Process mining for python (PM4Py): bridging the gap between process-and data science, pp 13–16

  6. Bloemen V, van de Pol Jaco MP, van der Aalst W (2018) Symbolically aligning observed and modelled behaviour. In: 18th international conference on application of concurrency to system design, ACSD, Bratislava, Slovakia, 25–29 June, pp 50–59

  7. Boltenhagen M, Chatain T, Carmona J (2019) Encoding conformance checking artefacts in SAT. In: Business process management workshops. Springer

  8. Boltenhagen M, Chatain T, Carmona J (2019) Generalized alignment-based trace clustering of process behavior. In: Proceedings of the 40th international conference on applications and theory of petri nets (ICATPN’19), number 11522 in Lecture Notes in Computer Science. Springer

  9. Buijs JCAM (2013) Loan application example. 4TU. Centre for Research Data. Dataset. doi.org/10.4121

  10. Carmona J, van Dongen BF, Solti A, Weidlich M (2018) Conformance checking—relating processes and models. Springer, Berlin

    Book  Google Scholar 

  11. Chatain T, Boltenhagen M, Carmona J (2019) Anti-alignments—measuring the precision of process models and event logs. Report hal-02383546: https://hal.inria.fr/hal-02383546

  12. Chatain T, Carmona J (2016) Anti-alignments in conformance checking—the dark side of process models. In: International conference on application and theory of petri nets and concurrency. Springer, pp 240–258

  13. Chatain T, Carmona J, Van Dongen B (2017) Alignment-based trace clustering. In: International conference on conceptual modeling. Springer, pp 295–308

  14. Davidson I, Ravi SS, Shamis L (2010) A sat-based framework for efficient constrained clustering. In: Proceedings of the 2010 SIAM international conference on data mining, pp 94–105. SIAM

  15. de Leoni M, Marrella A (2017) Aligning real process executions and prescriptive process models through automated planning. Expert Syst Appl 82:162–183

    Article  Google Scholar 

  16. García-Bañuelos L, van Beest NRTP, Dumas M, La Rosa M, Mertens W (2018) Complete and interpretable conformance checking of business processes. IEEE Trans Softw Eng 44(3):262–290

    Article  Google Scholar 

  17. Groce A, Chaki S, Kroening D, Strichman O (2006) Error explanation with distance metrics. Int J Softw Tools Technol Transf 8(3):229–247

    Article  Google Scholar 

  18. Ignatiev A, Morgado A, Marques-Silva J (2018) PySAT: a Python toolkit for prototyping with SAT oracles. In: SAT, pp 428–437

  19. Lee WLJ, Verbeek HMW, Munoz-Gama J, van der Aalst WMP, Sepúlveda M (2018) Recomposing conformance: closing the circle on decomposed alignment-based conformance checking in process mining. Inf Sci 466:55–91

    Article  Google Scholar 

  20. Leemans SJJ, Fahland D, van der Aalst WMP (2018) Scalable process discovery and conformance checking. Softw Syst Model 17(2):599–631

    Article  Google Scholar 

  21. Leemans Sander JJ, Fahland D, van der Aalst WMP (2013) Discovering block-structured process models from event logs containing infrequent behaviour. In: International conference on business process management. Springer, pp 66–78

  22. Métivier J-P, Boizumault P, Crémilleux B, Khiari M, Loudni S (2012) Constrained clustering using sat. In: International symposium on intelligent data analysis. Springer, pp 207–218

  23. Munoz-Gama J, Carmona J, Van Der Aalst WMP (2014) Single-entry single-exit decomposed conformance checking. Inf Syst 46:102–122

    Article  Google Scholar 

  24. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–574

    Article  Google Scholar 

  25. Reißner D, Conforti R, Dumas M, La Rosa M, Armas-Cervantes A (2017) Scalable conformance checking of business processes. In: OTM CoopIS, Rhodes, Greece, pp 607–627

  26. Tax N, Xixi L, Sidorova N, Fahland D, van der Aalst WMP (2018) The imprecisions of precision measures in process mining. Inf Process Lett 135:1–8

    Article  MathSciNet  Google Scholar 

  27. Taymouri F, Carmona J (2016) Model and event log reductions to boost the computation of alignments. In: Proceedings of the 6th international symposium on data-driven process discovery and analysis (SIMPDA 2016), Graz, Austria, 15–16 Dec 2016, pp 50–62

  28. Taymouri F, Carmona J (2016) A recursive paradigm for aligning observed behavior of large structured process models. In: 14th international conference of business process management (BPM), Rio de Janeiro, Brazil, Sept 18–22

  29. van der Aalst WMP (2013) Decomposing petri nets for process mining: a generic approach. Distrib Parallel Databases 31(4):471–507

    Article  Google Scholar 

  30. van Dongen BF (2018) Efficiently computing alignments—using the extended marking equation. In: Business process management—16th international conference, BPM 2018, Sydney, NSW, Australia, 9–14 Sept 2018, Proceedings, pp 197–214

  31. van Dongen BF, Carmona J, Chatain T (2016) A unified approach for measuring precision and generalization based on anti-alignments. In: Business process management—14th international conference, BPM 2016, Rio de Janeiro, Brazil, 18–22 Sept 2016. Proceedings, pp 39–56

  32. van Dongen BF, Carmona J, Chatain T, Taymouri F (2017) Aligning modeled and observed behavior: a compromise between computation complexity and quality. In: Advanced information systems engineering—29th international conference, CAiSE 2017, Essen, Germany, 12–16 June 2017, Proceedings, pp 94–109

  33. Verbeek HMW, van der Aalst WMP (2016) Merging alignments for decomposed replay. Springer, Cham, pp 219–239

    Google Scholar 

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Acknowledgements

This work has been supported by Farman institute at ENS Paris-Saclay and by MINECO and FEDER funds under Grant TIN2017-86727-C2-1-R.

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Authors and Affiliations

  1. ENS Paris-Saclay, Cachan, France

    Mathilde Boltenhagen & Thomas Chatain

  2. Universitat Politècnica de Catalunya, Barcelona, Spain

    Josep Carmona

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  1. Mathilde Boltenhagen
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  2. Thomas Chatain
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  3. Josep Carmona
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Correspondence to Josep Carmona.

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Boltenhagen, M., Chatain, T. & Carmona, J. Optimized SAT encoding of conformance checking artefacts. Computing 103, 29–50 (2021). https://doi.org/10.1007/s00607-020-00831-8

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