Brief description
Process mining is widely used to discover, analyze, and improve business processes based on event data extracted from IT systems, stored in so-called event logs. This has an important limitation, however; It limits the scope of analysis to back-end events. To avoid this problem the goal of this project, supported by the DFG (German Science Council), is to enable process discovery based on user interaction (UI) logs, rather than on traditional event logs. The project aims to develop approaches that address the challenges in an automated manner. It will advance state-of-the-art research in process mining, particularly for situations involving raw, low-level event data.
Project purpose
Process mining is widely used to discover, analyze, and improve business processes based on event data extracted from IT systems, stored in so-called event logs. A key task in this regard is process discovery, which aims to reconstruct how a process was truly executed. To do so, process discovery strives to establish an accurate process model based on the recorded behavior captured in an event log. Using such event logs as a basis for discovery has an important limitation, however: It limits the scope of analysis to back-end events, i.e., secondary, indirect events that are triggered by the actual user activity. User activities that do not result in such back-end events or take place in productivity applications such as Excel and Outlook, are thus not recorded in event logs and, therefore, invisible to traditional process mining and discovery techniques. To avoid this problem and be able to obtain a comprehensive view on business processes, the goal of this proposal is to enable process discovery based on user interaction (UI) logs, rather than on traditional event logs. In essence, a UI log is a collection of recorded interactions performed on GUI components, such as clicks on buttons or keyboard entries in text areas. The benefit of using UI logs is that they can be obtained for any business process of which the activities are performed on a computer, regardless of the specific applications required for it. Available logging software is then able to extract and store relevant data such as the interaction type (e.g., click or keyboard stroke), the time, and context (e.g., the GUI element and URL) in a UI log. The project will result in the development of approaches that address the challenges in an automated manner, ultimately covering the entire pipeline from UI log to an informative process representation. This is to be achieved by combining behavioral page 3 of 23 process analysis with a novel semantic angle, yielding approaches that overcome the limitations of existing works. In this way, the successful project will considerably advance state-of-the-art research in process mining, particularly for situations involving raw, low-level event data.
Subjects
Digital Transformation
Project partners
University of Mannheim, Kühne Logistics University (KLU)
