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• W2480922969 abstract "Underlying business processes in service management are people intensive and collaborative by nature. We are observing an emerging trend in the service management applications, moving away from rigid process orchestration to leveraging collaboration. Such solutions allow staffers to define their own customized, ad-hoc step flow consisting of the sequence of the activities necessary to handle a service component. These ad-hoc steps introduce uncertainty to the successful completion of a service request. When there is uncertainty, predictive guidance about future outcomes could provide value to the workers handling a time-sensitive service delivery component. Predicting the future outcomes using machine-learning techniques requires effective representation of the process execution traces. This is challenging when process model includes parallel execution flows or repeated executions of some activities. In this chapter, we describe algorithms for training machine learning models when the execution paths include parallel flows and when some activities are repeatedly executed." @default.
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• W2480922969 date "2015-08-12" @default.
• W2480922969 modified "2023-10-18" @default.
• W2480922969 title "Predictive Analytics for Business Processes in Service Management" @default.
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• W2480922969 doi "https://doi.org/10.4018/978-1-4666-8496-6.ch013" @default.
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