Tutorial: Analysing Event Data through Process Mining
Andrea Marrella (PhD in Engineering in Computer Science) is Assistant Professor at Sapienza Università di Roma. His research activity focuses on how to integrate Artificial Intelligence with Process Mining and Robotic Process Automation solutions, to untangle complex challenges such as the automated synthesis of process models, the automated adaptation of running processes and the optimal alignment of execution traces against their underlying (procedural or declarative) process models. Such topics are challenged in the application domains of smart manufacturing, healthcare, emergency management and cybersecurity. In 2017, he received the best paper award at the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017). He is acting as Information Director of ACM Journal on Data and Information Quality. He is Principal Investigator of two research projects funded by Sapienza Università di Roma, and he collaborated to several EU-funded research projects, including WORKPAD (FP6) and Smart Vortex (FP7). According to Google Scholar, his current h-index is 18.
ABSTRACT:
Process mining is a recent research discipline that sits between computational intelligence and data mining on the one hand, and process modeling and analysis on the other hand. Through process mining, decision makers can discover process models from event data, compare expected and actual behaviors, and enrich models with key information about their actual execution. This, in turn, provides the basis to understand, maintain, and enhance processes based on reality. In this tutorial, we introduce the process mining framework, the main process mining techniques and tools, and the different phases of event data analysis through process mining, discussing the various ways data and process analysts can make use of the mined models. Finally, we discuss common pitfalls and critical issues, so that everyone can start process mining right away.