| dc.description.abstract | Workflow management system (WFMS) enables the enterprise processes to be automatic, helping companies reduce costs, improving work efficiency, monitoring the work of the implementation and evaluating performance. With the transactions of business increase rapidly and the complexity of workflow enhance quickly, the activities in the workflow emergence more and more frequent time duration, the time duration make the schedule of activities can not be expected to complete and bring the business’ skill into full play. Therefore, this study mines out the activities of abnormal time duration from the historical records of the workflow, providing these to business executives as the basis for improvement of workflow.
Purpose of this paper is to accurately find all types of the time duration occurring in workflow, so we develop a Fuzzy Anomalous Duration Algorithm (FADA), based the important activity properties in the workflow to estimate the proper executive time of activities with fuzzy theory. This algorithm can more able to reflect the actual status of the workflow, and effectively identify all types of the time duration. We use the global logistics process of the stone industry to validate the accuracy of this algorithm, and compared the algorithm’s time duration result with Activity-based Algorithm for Mining Temporal Outlier (ABTO). The results show that this algorithm can correctly find all types of time duration, and the accuracy rate is much higher. The time duration results of this algorithm can be interpreted in the enterprise on a much higher proportion than ABTO, and more able to respond to the actual situation of workflow operations.
This paper’s algorithm (FADA) makes some improvements on the fuzzy inference system; it leaves out the step that needs experts’ judge. And it increases the developers’ flexibility in the design of fuzzy inference system.
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