Process Mining: When Process Intelligence meets Data Science

Teacher
Massimiliano de Leoni
Dipartimento di Matematica, Università degli Studi di Padova,
deleoni[at]math.unipd.it
INF/01

Aim

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 the curriculum on “Neuroscience, Technology, and Society”: the analysis of cognitive, behavioral processes of humans while interacting with, e.g., software tools, websites, IoT or health-care systems.

Syllabus

- 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 https://www.tf-pm.org/resources/manifesto in 16 languages, including English and Italian.

Course requirements
None

Examination modality
None

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

Schedule
16 November 2021, 10:00-12:00
18 November 2021, 10:00-12:00
19 November 2021, 10:00-12:00

Location
Room 2AB/45 at the Dept. of Mathematics, via Trieste 63 Padova

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