Leveraging Process-Mining Techniques

Semi-structured processes are data-driven, human-centric, flexible processes whose execution between instances can vary dramatically. Due to their unpredictability and data-driven nature, it´s becoming increasingly important to mine traces of events collected from these processes. This enables the extraction of mined process models that could help users handle new process instances. Process-mining techniques can help facilitate this goal, but it can be daunting for users new to process-aware analytics to sift through the literature and available software to determine which process-mining algorithm to use. The authors compare five process-mining algorithms and present a decision tree to help readers determine which mining algorithm to use for a specific problem. Semi-structured processes, however, present challenges that these mining techniques don´t address. So, the authors also identify three key characteristics of semi-structured processes and the mining challenges they present, highlighting a selection of emerging mining approaches that can address these challenges.