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Knowledge management and information extraction from structured and unstructured data for process mining (techniques, algorithms, and tools)
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Knowledge management and information extraction from structured and unstructured data for process mining (techniques, algorithms, and tools)
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Academic year 2023/2024
- Teachers
- Luigi Di Caro (Titolare del corso)
Emilio Sulis (Titolare del corso) - Degree course
- PhD in Computer Science
- Year
- 1st year
- Teaching period
- Annuale
- Type
- A scelta dello studente
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- INF/01 - informatics
- Delivery
- Tradizionale
- Language
- Inglese
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Sommario del corso
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Course objectives
The 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.
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Results of learning outcomes
Learning techniques and tools to extract, analyze, and visualize meaningful information from time series or temporal data.
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Program
The automatic analysis of timed events benefits from a broad set of methods, techniques and tools capable of extracting information from structured and unstructured data.
Applications are considerable to "processes" of very different types, e.g., educational, healthcare, legal, chatbot processes, and so on.
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Course delivery
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 PhD course "Knowledge management and information extraction from structured and unstructured data for process mining (techniques, algorithms, and tools)" consists of seminars alternating theory and practice.
The program will be carried out by the following teachers:
Emilio Sulis - Introduction to Process Mining algorithms, techniques, and tools (pm4py/disco/ProM)
Luigi Di Caro - Knowledge extraction from textual data, event log enrichmentThe e are pleased to offer specific insights by:
Chiara Difrancescomarino (University of Trento) - AI for BPM, Predictive process monitoring
Laura Genga (Technical University of Eindhoven) - Variant Analysis, Conformance checking techniquesThe 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.
COURSE SCHEDULE
Introduction to Process Mining, algorithms, tools
Variant Analysis, Conformance analysis, Repair
Event log enrichment
AI for BPMPredictive and Prescriptive process monitoring
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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 analyse data of interest, as well as use case studies/dataset provided by the lecturers.
Suggested readings and bibliography
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- Other
- Title:
- (suggested) Wil M. P. van der Aalst, Josep Carmona: Process Mining Handbook.
- Description:
- Lecture Notes in Business Information Processing 448, Springer 2022, ISBN 978-3-031-08847-6
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- Required:
- No
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There are no required textbooks for this course.
The lecturers will propose papers, documentation and websites as educational materials during the course.
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Notes
Course material will be provided by the teachers.
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