Abstract
The outcome of a business process (e.g., duration, cost, success rate) depends significantly on how well the assigned resources perform at their respective tasks. Currently, this assignment is typically based on a static resource query that specifies the minimum requirements (e.g., role) a resource has to meet. This approach has the major downside that any resource whatsoever that meets the requirements can be retrieved, possibly selecting resources that do not perform well on the task. To address this challenge, we present and evaluate in this paper a model-based approach that uses data integration and mining techniques for selecting resources based on their likely performance for the task or sub-process at hand.
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Niedermann, F., Pavel, A., Mitschang, B. (2011). Beyond Roles: Prediction Model-Based Process Resource Management. In: Abramowicz, W., Maciaszek, L., Węcel, K. (eds) Business Information Systems Workshops. BIS 2011. Lecture Notes in Business Information Processing, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25370-6_2
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DOI: https://doi.org/10.1007/978-3-642-25370-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25369-0
Online ISBN: 978-3-642-25370-6
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