Over the last decade, Process Mining has emerged as a new data science research field, sitting between machine learning and data mining on the one hand, and process modeling and analysis on the other hand. The goal of process mining is to discover, monitor and improve real end-to-end processes (i.e., not assumed processes) by extracting knowledge from event data readily available in today’s information systems. Process mining techniques have been applied to processes in a wide range of real-life contexts: health-care, public administration, education, airports, banking, logistic, to mention just a few examples.
Process Mining UC is the research group on process mining of the Pontificia Universidad Católica de Chile (UC), one of the top universities in Latin-America, lead by Prof. Marcos Sepúlveda and Prof. Jorge Munoz-Gama, composed by several students of all the levels (Doctorate, Master, and Undergraduate), and international collaborations. Process Mining UC focuses its research both in the theoretical aspects of process mining (developing new algorithms and techniques), and in the application of the current process mining techniques in specific domains, such as health-care and education (and developing methodologies and strategies for those domains).