Learning Business Rules for Adaptive Process Models

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Provided by: University of Leicester
Topic: Big Data
Format: PDF
This paper presents a new approach to handling knowledge-intensive business processes in an adaptive, flexible and accurate way. The authors propose to support processes by executing a process skeleton, consisting of the most important recurring activities of the process, through a workflow engine. This skeleton should be kept simple. The corresponding workflow is complemented by two features: firstly, a task management tool through which workflow tasks are delivered and that give human executors flexibility and freedom to adapt tasks by adding subtasks and resources as required by the context. And secondly, a component that learns business rules from the log files of this task management and that will predict subtasks and resources on the basis of knowledge from previous executions.
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