Advertisement

Fifty Shades of Green: How Informative is a Compliant Process Trace?

  • Andrea BurattinEmail author
  • Giancarlo Guizzardi
  • Fabrizio Maria Maggi
  • Marco Montali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

The problem of understanding whether a process trace satisfies a prescriptive model is a fundamental conceptual modeling problem in the context of process-based information systems. In business process management, and in process mining in particular, this amounts to check whether an event log conforms to a prescriptive process model, i.e., whether the actual traces present in the log are allowed by all behaviors implicitly expressed by the model. The research community has developed a plethora of very sophisticated conformance checking techniques that are particularly effective in the detection of non-conforming traces, and in elaborating on where and how they deviate from the prescribed behaviors. However, they do not provide any insight to distinguish between conforming traces, and understand their differences. In this paper, we delve into this rather unexplored area, and present a new process mining quality measure, called informativeness, which can be used to compare conforming traces to understand which are more relevant (or informative) than others. We introduce a technique to compute such measure in a very general way, as it can be applied on process models expressed in any language (e.g., Petri nets, Declare, process trees, BPMN) as long as a conformance checking tool is available. We then show the versatility of our approach, showing how it can be meaningfully applied when the activities contained in the process are associated to costs/rewards, or linked to strategic goals.

Keywords

Conformance checking Business value Process mining Goals 

Notes

Acknowledgements

The work is supported by Innovation Fund Denmark project EcoKnow.org (7050-00034A). The authors would like to thank Marlon Dumas for providing the inspiration for Fig. 1.

References

  1. 1.
    van der Aalst, W.M.: Process Mining, 2nd edn. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  2. 2.
    Adriansyah, A., van Dongen, B., van der Aalst, W.M.: Conformance checking using cost-based fitness analysis. In: Proceedings of EDOC, pp. 55–64. IEEE (2011)Google Scholar
  3. 3.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33606-5_19CrossRefGoogle Scholar
  4. 4.
    Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-99414-7CrossRefGoogle Scholar
  5. 5.
    Guizzardi, G.: On ontology, ontologies, conceptualizations, modeling languages. In: Frontiers in Artificial Intelligence and Applications, Databases and Information Systems. IOS Press (2007)Google Scholar
  6. 6.
    Guizzardi, R.S.S., Franch, X., Guizzardi, G.: Applying a foundational ontology to analyze means-end links in the \({\text{i}}^{*}\) framework. In: Proceedings of RCIS, pp. 1–11 (2012)Google Scholar
  7. 7.
    Liaskos, S., McIlraith, S.A., Sohrabi, S., Mylopoulos, J.: Representing and reasoning about preferences in requirements engineering. Requirements Eng. 16(3), 227–249 (2011)CrossRefGoogle Scholar
  8. 8.
    Munoz-Gama, J.: Conformance Checking and Diagnosis in Process Mining. LNBIP, vol. 270. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49451-7CrossRefzbMATHGoogle Scholar
  9. 9.
    Muñoz-Gama, J., Carmona, J.: A fresh look at precision in process conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15618-2_16CrossRefGoogle Scholar
  10. 10.
    Mylopoulos, J.: Conceptual modelling and telos. In: Conceptual Modelling, Databases, and CASE, pp. 49–68 (1992)Google Scholar
  11. 11.
    Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  12. 12.
    Sales, T.P., Guarino, N., Guizzardi, G., Mylopoulos, J.: An ontological analysis of value propositions. In: Proceedings of EDOC, pp. 184–193. IEEE Press (2017)Google Scholar
  13. 13.
    Vanden Broucke, S.K., De Weerdt, J., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)CrossRefGoogle Scholar
  14. 14.
    Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J.: Social Modeling for Requirements Engineering. MIT Press, Cambridge (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrea Burattin
    • 1
    Email author
  • Giancarlo Guizzardi
    • 3
  • Fabrizio Maria Maggi
    • 2
  • Marco Montali
    • 3
  1. 1.Technical University of DenmarkKgs. LyngbyDenmark
  2. 2.University of TartuTartuEstonia
  3. 3.Free-University of Bozen-BolzanoBolzanoItaly

Personalised recommendations