ResRec

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ResRec: Resource Recommendation in ProM

Description

Dynamically allocating the most appropriate resource to execute the diff erent activities of a business process is an important challenge in business process management. Selecting the most suitable resources is a challenge for those in charge of making the allocation, because the efficiency with which this task is executed, can contribute to the quality of the results, and improve the process performance.

ResRec, a novel Multi-factor Criteria tool that can be used to recommend and allocate resources dynamically.  ResRec is a decision maker-oriented approach that provides the feature of solving individual requests (On-demand), or requests made in blocks (Batch) through a recommender system developed in ProM.

 Screenshots

Import data

a_Import

Knowledge generation plug-in

a_KnowledgeGeneration

Recommend request plug-in

c_GenerateRecommendation

Configure request parameters

d_Configure

Generated Batch Resource Recommendation example

e_Result

Screencast

To demonstrate the use of ResRec tool, a screencast can be seen here:


The video shows the steps followed to generate the resource allocation and recommendation considering the proposed use cases: a) On-demand Resource Allocation, b) On-demand Resource Recommendation, c) Batch Resource Allocation, and d) Batch Resource Recommendation.

We used a Help-Desk process as a running example.  First, we add the contextual and the historical information required to evaluate the resources. Then, we create the knowledge base needed to perform the resource recommendation. After that, we con figure the corresponding parameters in order to generate a Batch Resource Recommendation, de fining a batch of requests and the weights describing the importance of each criterion. We show the obtained results by applying the method based on BPA2, performing a Batch Resource Recommendation. Additional to the Batch Resource Recommendation, the other three use cases outlined are also presented.

Event log example

A testing event log is available  here: LogHelpDeskConsultingFirm.

About

M. Arias, E. Rojas, J. Munoz-Gama, M. Sepulveda, A framework for recommending resource allocation based on process mining, in: Business Process Management Workshops – BPM 2015, 2015.

Acknowledgments

This project was partially supported by the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional 2014-63140181), Universidad de Costa Rica Professor Fellowships, and by Fondecyt (Chile) Project No.1150365.

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