An Optimal Approach for Workflow Staff Assignment Based on Hidden Markov Models

Authors:
Hedong Yang;Chaokun Wang;Yingbo Liu;Jianmin Wang
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, China 10084;Tsinghua National Laboratory for Information Science and Technology ( TNList ) School of Software, Tsinghua University, Beijing, China 10084;Department of Computer Science and Technology, Tsinghua University, Beijing, China 10084;Tsinghua National Laboratory for Information Science and Technology ( TNList ) School of Software, Tsinghua University, Beijing, China 10084
Venue:
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
Year:
2008

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Abstract

Staff assignment of workflow is often performed manually and empirically. In this paper we propose an optimal approach named SAHMM ( Staff Assignment based on Hidden Markov Models ) to allocate the most proficient set of employees for a whole business process based on workflow event logs. The Hidden Markov Model( HMM ) is used to describe the complicated relationships among employees which are ignored by previous approaches. The validity of the approach is confirmed by experiments on real data.