Knowledge management and information extraction from structured and unstructured data for process mining (techniques, algorithms, and tools)

The discipline of Process Mining has recently captured increasing attention, extending from organizational and management studies to all areas where data with timed events are present.

The automated analysis of timed events benefits from a broad set of methods, techniques and tools capable of extracting information from structured and unstructured data.

The PhD course will provide an overview of main topics in the Process Mining discipline to discover and analyze temporal processes, including practical techniques and tools for visualizing processes, identifying bottlenecks, performing variant analysis, introducing predictive process monitoring, comparing time series data in a ‘conformance checking’ perspective, processing data to extract information from text in event log format.

Applications are considerable to "processes" of very different types, e.g., educational, healthcare, legal, chatbot processes, and so on.

Scheduling of course lectures:

  • I lesson - May 17 - 2:30 p.m. - 5:30 p.m. (Third floor, Sala riunioni)
    LINK Webex: 

  • II lesson - May 21 - 2:30 p.m. - 5:30 p.m. (Third floor, Sala riunioni)
    LINK:

  • III lesson - May 23 - 2:30 p.m. - 5:30 p.m. (Third floor, Sala riunioni)
    LINK:

  • IV lesson - Jun 5 - 10 - 13 (First floor, Sala Seminari)
    LINK:

  • V lesson - Jun 6 - 10 - 13 (First floor, Sala Seminari)
    LINK:

  • VI lesson - TBA

  • VII lesson - TBA
  • VIII lesson - Jun 6 - 14-17 p.m. (First floor, Sala seminari)

Program

The PhD course consists of seminars alternating theory and practice carried out by the following teachers:

Emilio Sulis - Introduction to Process Mining algorithms, techniques, and tools (pm4py/ProM/bupaR/DISCO)
Luigi Di Caro - Knowledge extraction from textual data, event log enrichment
Laura Genga (Technical University of Eindhoven) - Variant Analysis, Conformance checking techniques
Chiara Difrancescomarino (University of Trento) - A discussion on AI and PM

The course introduces the main techniques for process discovery, validation and improvement from event-logs, typically extracted from information systems, sensors, web applications.

"Predictive process monitoring" and NLP techniques for feature set extraction and enrichment will be introduced, including a combination of data mining, text mining, and process analysis.

Teaching material

Course material will be provided by the teachers. There are no required textbooks for this course.
The lecturers will propose papers, documentation and websites as educational materials during the course.

Learning assessment methods

The examination consists of a written presentation (e.g., a set of slide or a short paper) concerning the student's preferred argument/topic.
It is also possible to analyse data of interest, as well as use case studies/dataset provided by the lecturers.

Suggested readings and bibliography

(suggested) Wil M. P. van der Aalst, Josep Carmona: Process Mining Handbook, Springer 2022, ISBN 978-3-031-08847-6 - https://link.springer.com/book/10.1007/978-3-031-08848-3