الفهرس 
Title : A human-resource recommendation approach based on enterprise historical data
ContentsOnly 14 pages are availabe for public view
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Abstract
Recommending the appropriate resource to execute the next activity of a running process instance is of utmost importance for the overall performance of the process, the resource, and the whole organization. Several approaches have been proposed to recommend a resource based on the task requirements and the resource capabilities. Moreover, the process execution history and logs have been used to better recommend a resource based on di erent criteria like the frequency, and the speed of execution, etc. All these approaches have recommended resources based on the history of execution of the single task to which a resource is allocated. In this thesis, we have introduced a novel approach that looks into the co-working history of resources.It considers the resources that had executed the previous tasks in the current running process instances.The aim is to recommend a resource that has the best harmony with the rest of the other resources. We have used the process logs as input and implemented the Best Position Algorithms BPA and BPA2 with our approach to recommend the resource that has the highest overall score