Process Mining: When Data Science Meets Process Intelligence

Massimiliano DeLeoni
Department of Mathematics

Process mining is a research discipline that sits between Data Science and Machine Learning, on the one hand, and Social Science, on the other hand. It aims to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today's systems. Process Mining is applicable in all those domains where there is an implicit or explicit process. No assumption is made on the nature of these processes, besides that the process is expected to start from an initial state and reach an expected goal, passing through a sequence of action steps. Process Mining has often been applied to analyze traditional business processes that are executed in financial institutions, city halls, hospitals, etc. However, case studies have also been carried on to examine processes that bring together humans, robots, systems and their interactions. This is a strong link to curriculum on “Neuroscience, Technology, and Society”: the analysis of cognitive, behavioral processes of humans while interacting with, e.g., software tools, web sites, IoT or health-care systems.

- Introduction to Process Mining
- Process Discovery from Event Data
- Dotted Chart Analysis of Event Logs
- Hands-on Session with ProM and Disco

Introductory reading

The Process Mining Manifesto available at in 15 languages, including English and Italian.

Course material, enrollment and last minute notifications
Made available by the teacher at this Moodle address

Course requirements

14 July 2020, 10:00-13:00
17 July 2020, 10:00-13:00

Via Zoom. The teacher will provide the link to the meeting from within the Moodle site of this course.

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