Mining Event Logs to Support Workflow Resource Allocation

TL;DR

This paper introduces a data mining method using an Apriori-like algorithm to automatically discover resource allocation rules from event logs, aiming to enhance workflow resource management efficiency.

Contribution

It presents a novel application of association rule mining with correlation measures for automated resource allocation in workflows, improving over manual methods.

Findings

The approach achieves high accuracy in resource recommendation.

It outperforms traditional classifiers like C4.5, SVM, ID3, and Naive Bayes.

Effective in identifying relevant resource allocation patterns.

Abstract

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, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.