Currently, workflow technology is widely used to facilitate the working process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Naï ve Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.
Paper REF presents a data mining approach to address the resource allocation problem, which can find the frequent patterns from the event log, and then annotate and chose an optimal resource allocation rule by using a correlation measure named lift.
Mining Event Logs to Support Workflow Resource Allocation
Knowledge-based Systems 35(2012) 320-331
Computer Science