Workflow Quality Analysis System Based On Process Logs Mining: Design And Implementation

To improve the performance and service quality of workflows, Intelligent WfMS should provide the ability of Process Quality Analysis. WfMS logs every event that occurs during processes execution. Therefore, workflow logs include a significant amount of information that can be used to analyze process executions, understand the causes of high- and low-quality process executions. In this thesis, I develop a Workflow Quality Analysis System (WfQAS), which deploy data warehousing and mining techniques, to "mine" useful "knowledge" from a large volume of workflow logs for analyzing the reasons of the "good" and "bad" of processes.WfQAS is comprised of data warehouse model on OLAP technology, correlations among behaviors' mining model and analysis of process exceptions model.Data warehouse model based on OLAP technology set up a data warehouse by using the huge process logs, on which we can use OLAP, and master the quality of some process from different aspects such as times and resources as participants.Correlations among behaviors' mining model, based on the predefined Behavior Patterns, which are usually related to quality of processes. WfQAS perform algorithm Apriori to mine correlations among behaviors.Analysis of process exceptions model, analyze exceptions i.e., of deviations from the desired or acceptable behavior, by decision tree classification algorithm.