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On Knowledge Transfer from Cost-Based Optimization of Data-Centric Workflows to Business Process Redesign

Part of the Lecture Notes in Computer Science book series (TLDKS,volume 12130)

Abstract

This work deals with redesigning business process models, e.g., in BPMN, based on cost-based optimization techniques that were initially proposed for data analytics workflows. More specifically, it discusses execution cost and cycle time improvements through treating business processes in the same way as data-centric workflows. The presented solutions are cost-based, i.e., they employ quantitative metadata and cost models. The advantage of this approach is that business processes can benefit from recent advances in data-intensive workflow optimization similarly to the manner they nowadays benefit from additional data analytics areas, e.g., in the area of process mining. Concrete use cases are presented that are capable of demonstrating that even in small, more conservative cases, the benefits are significant. The contribution of this work is to show how to automatically optimize the model structure of a given process in terms of the ordering of tasks and how to perform resource allocation under contradicting objectives. Finally, the work identifies open issues in developing end-to-end business process redesign solutions with regards to the case studies considered.

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Notes

  1. 1.

    Note that these constraints refer to the process model structure; additional execution constraints, e.g., two tasks share the same resource and thus cannot run simultaneously, affect the cost models that quantify the optimization objectives (see also Sect. 4.2).

  2. 2.

    taken from https://www.businessprocessincubator.com/.

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Kougka, G., Varvoutas, K., Gounaris, A., Tsakalidis, G., Vergidis, K. (2020). On Knowledge Transfer from Cost-Based Optimization of Data-Centric Workflows to Business Process Redesign. In: Hameurlain, A., Tjoa, A. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII. Lecture Notes in Computer Science(), vol 12130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62199-8_3

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