An approach to recommend resources for business processes

Abstract

Workflow management is an important technology of business process management that links tasks and qualified resources as a bridge. Researches have been carried out to improve the resource allocation of workflow that is often performed manually and empirically either by mining resource allocation rules or by optimizing the resource allocation for tasks to achieve certain goals such as minimal cost or duration. None of these approaches can guarantee to give the suitable solution to resource allocators because of the dynamic natures of business process executions. In this paper we propose an approach, BNRR (Bayesian Network-based Resource Recommendation), to recommend the most proficient sets of resources for a business process based on event logs, which gives the allocators chances to find the most suitable solution. Our approach considers both the information about the resource dependency and the information about the resource capability. The approach can be applied to recommend resources either for a whole workflow or for an individual task. The approach is validated by experiments on real life data. © 2012 Springer-Verlag.