Mining Event Logs to Support Workflow Resource Allocation
This addresses inefficiencies in manual resource allocation for enterprise workflow systems, though it is incremental as it builds on existing data mining techniques.
The paper tackled the resource allocation problem in workflow management by proposing a data mining approach using an Apriori-like algorithm to generate rules from event logs, and it showed effectiveness in accuracy and recommendations compared to methods like C4.5 and SVM.
Workflow technology is widely used to facilitate the business 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, Naïve Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.