Constructing Probabilistic Process Models Based on Hidden Markov Models for Resource Allocation
A Hidden Markov Model (HMM) is a temporal statistical model which is widely utilized for various applications such as gene prediction, speech recognition and localization prediction. HMM represents the state of the process in a discrete variable, where the values are the possible observations of the world. For the purpose of process mining for resource allocation, HMM can be applied to discover a probabilistic workflow model from activities and identify the observations based on the resources utilized by each activity. In this paper, we introduce a process discovery method that combines an organizational perspective with a probabilistic approach to address the resource allocation and improve the productivity of resource management, maximizing the likelihood of the model using the Expectation-Maximization procedure.
KeywordsProbabilistic process discovery Process mining Resource allocation Hidden markov models
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2012R1A1B4003505).
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