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Data-Aware Conformance Checking for Declarative Business Process Models

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Abstract

Since the accurate management of business processes is receiving increasing attention, conformance checking, i.e., verifying whether the observed behaviour matches a modelled behaviour, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, little work has been conducted to deal with conformance checking of declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any diagnostics. To enhance the accurate management of flexible business processes, this work presents a constraint-based approach for conformance checking over declarative models (including control-flow and data perspectives) and for providing related diagnosis.

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Notes

  1. 1.

    Note that conformance checking is one of the three main application scenarios of process mining.

  2. 2.

    In a similar way, [11] deals with modelling data in ConDec.

  3. 3.

    This model is an adaptation of the model presented in [12].

  4. 4.

    For the sake of clarity, only completed events for activity executions are included in the trace representation.

  5. 5.

    Note that the CSP variables st and et allows dealing with non-atomic activities.

  6. 6.

    All the instances which are generated for the empirical evaluation can be accessed at http://www.lsi.us.es/-quivir/index.php/Main/Downloads.

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Correspondence to Diana Borrego .

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Borrego, D., Barba, I., Abad, P. (2014). Data-Aware Conformance Checking for Declarative Business Process Models. In: José Escalona, M., Aragón, G., Linger, H., Lang, M., Barry, C., Schneider, C. (eds) Information System Development. Springer, Cham. https://doi.org/10.1007/978-3-319-07215-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-07215-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07214-2

  • Online ISBN: 978-3-319-07215-9

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