Abstract
Business processes are designed with the assumption that the data used by the process is an accurate reflection of reality. However, this assumption does not always hold, and situations of data inaccuracy might occur which bear substantial consequences to the process and to business goals. Until now, data inaccuracy has mainly been addressed in the area of business process management as a possible exception at runtime, to be resolved through exception handling mechanisms. Design-time analysis of potential data inaccuracy has been mostly overlooked so far. In this paper we propose a conceptual framework for incorporating data inaccuracy considerations in process models to support an analysis of data inaccuracy at design time and empirically evaluate its usability by process designers.
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Notes
- 1.
We make a basic assumption here that the IS data structure is well-designed, which means that all relevant domain variables are represented by corresponding data items.
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Acknowledgement
The first and the second authors are supported by the Israel Science Foundation under grant agreement no. 856/13. The third author is supported by the Israel Science Foundation under grant agreement no. 817/15.
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Evron, Y., Soffer, P., Zamansky, A. (2017). Incorporating Data Inaccuracy Considerations in Process Models. In: Reinhartz-Berger, I., Gulden, J., Nurcan, S., Guédria, W., Bera, P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2017 2017. Lecture Notes in Business Information Processing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-59466-8_19
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