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Improving Understandability of Declarative Process Models by Revealing Hidden Dependencies

  • Johannes De Smedt
  • Jochen De Weerdt
  • Estefanía Serral
  • Jan Vanthienen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Declarative process models have become a mature alternative to procedural ones. Instead of focusing on what has to happen, they rather follow an outside-in approach based on a rule base containing different types of constraints. The models are well-capable of representing flexible behavior, as everything that is not forbidden by the constraints in the model is possible during execution. These models, however, are more difficult to comprehend and require a higher mental effort of both the modeler and the reader. Since constraints can be added freely to the model, it is often overseen what impact the combination of them has. This is often referred to as hidden dependencies. This paper proposes a methodology to make these dependencies explicit for the declarative process modeling language Declare by considering a Declare model as a graph and relying on the constraints’ characteristics. Moreover, this paper also contributes by empirically confirming that a tool that can visualize hidden dependency information on top of a Declare model has a significant positive impact on the understandability of Declare models.

Keywords

Declarative process modeling Declare Hidden dependencies Empirical evaluation 

References

  1. 1.
    Zugal, S., Pinggera, J., Weber, B., Mendling, J., Reijers, H.A.: Assessing the impact of hierarchy on model understandability – a cognitive perspective. In: Kienzle, J. (ed.) MODELS 2011 Workshops. LNCS, vol. 7167, pp. 123–133. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Haisjackl, C., Zugal, S., Soffer, P., Hadar, I., Reichert, M., Pinggera, J., Weber, B.: Making sense of declarative process models: common strategies and typical pitfalls. In: Nurcan, S., Proper, H.A., Soffer, P., Krogstie, J., Schmidt, R., Halpin, T., Bider, I. (eds.) BPMDS 2013 and EMMSAD 2013. LNBIP, vol. 147, pp. 2–17. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    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, pp. 287–300. IEEE (2007)Google Scholar
  4. 4.
    Zugal, S., Pinggera, J., Weber, B.: The impact of testcases on the maintainability of declarative process models. In: Halpin, T., Nurcan, S., Krogstie, J., Soffer, P., Proper, E., Schmidt, R., Bider, I. (eds.) BPMDS 2011 and EMMSAD 2011. LNBIP, vol. 81, pp. 163–177. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Montali, M., Pesic, M.M., Aalst, W., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographies. ACM Trans. Web 4(1), 1–62 (2010)CrossRefGoogle Scholar
  6. 6.
    De Smedt, J., De Weerdt, J., Vanthienen, J.: Fusion miner: process discovery for mixed-paradigm models. Decis. Support Syst. 77, 123–136 (2015)CrossRefGoogle Scholar
  7. 7.
    Burattin, A., Maggi, F.M., van der Aalst, W.M., Sperduti, A.: Techniques for a posteriori analysis of declarative processes. In: 2012 IEEE 16th International Enterprise Distributed Object Computing Conference, pp. 41–50. IEEE (2012)Google Scholar
  8. 8.
    Pesic, M.: Constraint-based workflow management systems: shifting control to users. Ph.D. thesis, Technische Universiteit Eindhoven (2008)Google Scholar
  9. 9.
    Di Ciccio, C., Mecella, M.: A two-step fast algorithm for the automated discovery of declarative workflows. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 135–142. IEEE (2013)Google Scholar
  10. 10.
    Westergaard, M., Stahl, C., Reijers, H.A.: Unconstrainedminer: efficient discovery of generalized declarative process models. Technical report BPM-13-28, BPMcenter (2013)Google Scholar
  11. 11.
    Maggi, F.M., Montali, M., Westergaard, M., van der Aalst, W.M.P.: Monitoring business constraints with linear temporal logic: an approach based on colored automata. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 132–147. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    De Smedt, J., De Weerdt, J., Vanthienen, J., Poels, G.: Mixed-paradigm process modeling with intertwined state spaces. Bus. Inf. Syst. Eng. 58(1), 19–29 (2016)CrossRefGoogle Scholar
  13. 13.
    Westergaard, M., Maggi, F.M.: Declare: a tool suite for declarative workflow modeling and enactment. BPM (Demos) 820, 1–5 (2011)Google Scholar
  14. 14.
    Møller, A.: dk. brics. automaton-finite-state automata and regular expressions for Java (2010). Accessed 30 Aug 2014Google Scholar
  15. 15.
    van der Aalst, W.M.P., Pesic, M.: DecSerFlow: towards a truly declarative service flow language. In: Bravetti, M., Núñez, M., Zavattaro, G. (eds.) WS-FM 2006. LNCS, vol. 4184, pp. 1–23. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Hildebrandt, T.T., Mukkamala, R.R.: Declarative event-based workflow as distributed dynamic condition response graphs. In: PLACES, pp. 59–73 (2011)Google Scholar
  18. 18.
    Hull, R., et al.: Introducing the guard-stage-milestone approach for specifying business entity lifecycles (invited talk). In: Bravetti, M. (ed.) WS-FM 2010. LNCS, vol. 6551, pp. 1–24. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    De Smedt, J., De Weerdt, J., Serral Asensio, E., Vanthienen, J.: Gamification of declarative process models for learning and model verification. In: Business Process Management Workshops. Springer (2015). AcceptedGoogle Scholar
  20. 20.
    De Giacomo, G., Masellis, R.D., Montali, M.: Reasoning on LTL on finite traces: insensitivity to infiniteness. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1027–1033, 27–31 July 2014, Québec City, Québec, Canada (2014)Google Scholar
  21. 21.
    Di Ciccio, C., Mecella, M., Mendling, J.: The effect of noise on mined declarative constraints. In: Ceravolo, P., Accorsi, R., Cudre-Mauroux, P. (eds.) Data-Driven Process Discovery and Analysis, pp. 1–24. Springer, Heidelberg (2015)Google Scholar
  22. 22.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: A knowledge-based integrated approach for discovering and repairing declare maps. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 433–448. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Johannes De Smedt
    • 1
  • Jochen De Weerdt
    • 1
  • Estefanía Serral
    • 1
  • Jan Vanthienen
    • 1
  1. 1.Department of Decision Sciences and Information Management, Faculty of Economics and BusinessKU LeuvenLeuvenBelgium

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