An Optimal Approach for Workflow Staff Assignment Based on Hidden Markov Models
作者： Hedong Yang , Chaokun Wang , Yingbo Liu , Jianmin Wang , None
DOI: 10.1007/978-3-540-88875-8_12
关键词: Hidden Markov model 、 Computer science 、 Artificial intelligence 、 Data mining 、 Set (abstract data type) 、 Machine learning 、 Workflow 、 Event (computing) 、 Business process
摘要: Staff assignment of workflow is often performed manually and empirically. In this paper we propose an optimal approach named SAHMM ( Assignment based on Hidden Markov Models ) to allocate the most proficient set employees for a whole business process event logs. The Model( HMM used describe complicated relationships among which are ignored by previous approaches. validity confirmed experiments real data.
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