Workflow technology is widely used to facilitate the business process inenterprise information systems (EIS), and it has the potential to reduce designtime, enhance product quality and decrease product cost. However, significantlimitations still exist: as an important task in the context of workflow, manypresent resource allocation operations are still performed manually, which aretime-consuming. This paper presents a data mining approach to address theresource allocation problem (RAP) and improve the productivity of workflowresource management. Specifically, an Apriori-like algorithm is used to findthe frequent patterns from the event log, and association rules are generatedaccording to predefined resource allocation constraints. Subsequently, acorrelation measure named lift is utilized to annotate the negativelycorrelated resource allocation rules for resource reservation. Finally, therules are ranked using the confidence measures as resource allocation rules.Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes andthe presented approach, and the results show that the presented approach iseffective in both accuracy and candidate resource recommendations.
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