Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage

Abstract

Process mining enables organizations to analyze the data stored in their information systems and derive insights regarding their business processes. However, raw data needs to be converted into a format that can be fed into process mining algorithms. Various pre-analysis activities can be performed on the raw data, such as imperfection removal or granularity level change. Although pre-analysis activities play a crucial role in process mining, there is currently a limited overview available regarding their scope and the extent of their examination. This study presents a systematic literature review of the pre-analysis activities in process mining projects. To better understand this stage and its current state of research, we explore which activities constitute the pre-analysis stage, their goals, the applied research methodologies, the proposed research outcomes, and the data used to evaluate the research outcomes. We identify 15 pre-analysis activities and concepts, e.g., data extraction, generation, and cleaning. We also discover that design science research is the methodology and methods that are the primary research outcome in previous studies. We also realize that the proposed outcomes have been evaluated using only real-life data most of the time. This study reveals that research on pre-analysis is a growing ield of interest in process mining.