Detection of Declarative Process Constraints in LTL Formulas

  • Nicolai SchützenmeierEmail author
  • Martin Käppel
  • Sebastian Petter
  • Stefan Schönig
  • Stefan Jablonski
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 366)


Declarative process models consist of temporal constraints that a process must satisfy during execution. Constraint templates are patterns that define parameterized classes of properties. Their semantics can be formalized using formal logics such as Linear Temporal Logic (LTL) over finite traces. There exists a big amount of different constraint templates for different purposes. In practice, the variety of different templates yields complexity and performance issues with regard to model comparison, compliance checking and in particular process mining. In this paper we give a comprehensively overview about existing declare templates and transform their underlying LTL formula into the positive normal form (PNF), a canonical standard form for LTL formulas. On this basis, we present an algorithm for detecting declare templates in any LTL formula fulfilling the conditions for PNF. We reduce the number of process constraints that have to be proven by the algorithm to speed up the runtime and give some advice for further optimizations.


Declarative process management Declare Linear temporal logic Positive normal form 


  1. 1.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). Scholar
  2. 2.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: full support for loosely-structured processes. In: IEEE EDOC Conference 2007, pp. 287–300 (2007)Google Scholar
  3. 3.
    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)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Schönig, S., Ackermann, L., Jablonski, S.: Towards an implementation of data and resource patterns in constraint-based process models. In: Modelsward, pp. 271–278 (2018)Google Scholar
  5. 5.
    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
  6. 6.
    Maggi, F.M., Mooij, A., van der Aalst, W.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199 (2011)Google Scholar
  7. 7.
    De Smedt, J., Weerdt, J., Vanthienen, J., Poels, G.: Mixed-paradigm process modeling with intertwined state spaces. Bus. Inf. Syst. Eng. 58, 12 (2015)Google Scholar
  8. 8.
    Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006). Scholar
  9. 9.
    Baier, C., Katoen, J.-P.: Principles of Model Checking. Representation and Mind Series. The MIT Press, Cambridge (2008)zbMATHGoogle Scholar
  10. 10.
    Emerson, E.A.: Temporal and modal logic. In: Formal Models and Semantics, pp. 995–1072. Elsevier (1990)Google Scholar
  11. 11.
    Fornara, N., Colombetti, M.: Specifying artificial institutions in the event calculus. In: Handbook of Research on Multi-agent Systems: Semantics and Dynamics of Organizational Models, pp. 335–366. IGI Global (2009)Google Scholar
  12. 12.
    Pesic, M., Schonenberg, H., Van der Aalst, W.M.: Declare: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), p. 287. IEEE (2007)Google Scholar
  13. 13.
    Bernardi, M.L., Cimitile, M., Di Francescomarino, C., Maggi, F.M.: Using discriminative rule mining to discover declarative process models with non-atomic activities. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 281–295. Springer, Cham (2014). Scholar
  14. 14.
    Baumann, M., Baumann, M.H., Schönig, S., Jablonski, S.: Resource-aware process model similarity matching. In: ICSOC 2014 Workshops, pp. 96–107 (2014)CrossRefGoogle Scholar
  15. 15.
    Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. In: Inductive Logic Programming, pp. 132–146 (2007)Google Scholar
  16. 16.
    Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 278–295. Springer, Heidelberg (2009). Scholar
  17. 17.
    Westergaard, M., Maggi, F.M.: Looking into the future: using timed automata to provide a priori advice about timed declarative process models. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 250–267. Springer, Heidelberg (2012). Scholar
  18. 18.
    Montali, M., Chesani, F., Mello, P., Maggi, F.M.: Towards data-aware constraints in declare. In: SAC, pp. 1391–1396. ACM (2013)Google Scholar
  19. 19.
    Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)CrossRefGoogle Scholar
  20. 20.
    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). Scholar
  21. 21.
    Ackermann, L., Schönig, S., Jablonski, S.: Simulation of multi-perspective declarative process models. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 61–73. Springer, Cham (2017). Scholar
  22. 22.
    Skydanienko, V., Francescomarino, C.D., Maggi, F.: A tool for generating event logs from multi-perspective declare models. In: BPM (Demos) (2018)Google Scholar
  23. 23.
    Ackermann, L., Schönig, S., Petter, S., Schützenmeier, N., Jablonski, S.: Execution of multi-perspective declarative process models. In: OTM 2018 Conferences, pp. 154–172 (2018)Google Scholar
  24. 24.
    van der Aalst, W., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. In: CSRD, pp. 99–113 (2009)Google Scholar
  25. 25.
    Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographies. ACM Trans. Web 4(1), 3 (2010)CrossRefGoogle Scholar
  26. 26.
    Burattin, A., Maggi, F.M., van der Aalst, W.M., Sperduti, A.: Techniques for a posteriori analysis of declarative processes. In: EDOC, Beijing, pp. 41–50. IEEE, September 2012Google Scholar
  27. 27.
    Latvala, T., Biere, A., Heljanko, K., Junttila, T.: Simple bounded LTL model checking. In: Hu, A.J., Martin, A.K. (eds.) FMCAD 2004. LNCS, vol. 3312, pp. 186–200. Springer, Heidelberg (2004). Scholar
  28. 28.
    Tauriainen, H.: Automata and linear temporal logic: translations with transition-based acceptance, January 2006Google Scholar
  29. 29.
    Namjoshi, K.S.: An efficiently checkable, proof-based formulation of vacuity in model checking. In: Alur, R., Peled, D.A. (eds.) CAV 2004. LNCS, vol. 3114, pp. 57–69. Springer, Heidelberg (2004). Scholar
  30. 30.
    Knuth, D.E., Morris, J.H., Pratt, V.R.: Fast pattern matching in strings. SIAM J. Comput. 6, 323–350 (1977)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicolai Schützenmeier
    • 1
    Email author
  • Martin Käppel
    • 1
  • Sebastian Petter
    • 1
  • Stefan Schönig
    • 1
  • Stefan Jablonski
    • 1
  1. 1.Institute for Computer ScienceUniversity of BayreuthBayreuthGermany

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