Local Concurrency Detection in Business Process Event Logs

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

Anomaly Detection Algorithms in Business Process Logs

In some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offe...

Comparing Business Process Variants Using Models and Event Logs

Organizations realize that benefits can be achieved by closely working together on the design of their business processes. But even when there is a joint design for a particular business process, the way individual organizations carry out that process may differ – either wittingly or unwittingly. This paper proposes an analytical approach that helps to compare how different organizations execut...

Log Delta Analysis: Interpretable Differencing of Business Process Event Logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose difference...

Utility-Based Control Flow Discovery from Business Process Event Logs