Abstract
Process mining is a paradigm shift from traditional process understanding methodologies like interviews and surveys to a data-driven understanding of the actual digital processes. It analyzes business processes by applying algorithms to the event data generated by digital systems. The chapter provides insight into various uses of process mining in different social and economic processes, with examples from past works demonstrating how practical process mining is in detecting and mitigating bottlenecks in these sectors. Then the chapter further delves into the details of process mining algorithms, key features, and metrics that can help practitioners and researchers evaluate process mining for their work. It also highlights some data quality issues in the event log that can inhibit obtaining fair results from process models. Additionally, some current limitations and concerns are described for creating awareness and building over the body of knowledge in the process and sequential mining techniques.
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