A Method of Business Process Bottleneck Detection In order to improve the efficiency of business processes and ensure the timeliness of cases, an approach for bottleneck detection is proposed, which gives a detailed definition of bottleneck in business process. The approach starts from the overall performance of the system and reduces process congestion by detecting and relieving bottlenecks, which is based on the event log analysis. By extracting relevant information like task arrival rate and maximum service rate etc. from the event log, this approach can analyze the historical trends of congestion rate of each task. And finally it combines the task completion time and historical congestion to detect bottleneck. Experiments show that the bottleneck detection method based on event log can better identify the bottleneck in the business process, and solving the bottleneck can effectively improve the case completion rate and average completion time of the process.

Fast and accurate business process drift detection Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs xe2x80x93 also known as business process drift detection xe2x80x93 enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a tradeoff between classification accuracy and drift detection delay. A validation on synthetic and reallife logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.