Process Mining (Wintersemester 2022/2023)
Dozent:
Prof. Dr. Henrik Leopold
(Business Process Technology)
Allgemeine Information
- Semesterwochenstunden: 2
- ECTS: 3
- Benotet:
Ja
- Einschreibefrist: 01.10.2022 - 31.10.2022
- Prüfungszeitpunkt §9 (4) BAMA-O: 17.02.2023
- Lehrform: Vorlesung / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-K Konzepte und Methoden
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-S Spezialisierung
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- DAPP: Data Applications
- HPI-DAPP-K Konzepte und Werkzeuge
- DAPP: Data Applications
- HPI-DAPP-T Techniken und Werkzeuge
- DAPP: Data Applications
- HPI-DAPP-S Spezialisierung
- SECA: Security Analytics
- HPI-SECA-K Konzepte und Methoden
- SECA: Security Analytics
- HPI-SECA-T Techniken und Werkzeuge
- SECA: Security Analytics
- HPI-SECA-S Spezialisierung
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-T Technologies and Tools
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-S Specialization
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-C Concepts and Methods
- HPI-SSE-S Systems Foundations
- SSYS: Software Systems
- HPI-SSYS-C Concepts and Methods
- SSYS: Software Systems
- HPI-SSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- MODA: Models and Algorithms
- HPI-MODA-C Concepts and Methods
- MODA: Models and Algorithms
- HPI-MODA-T Technologies and Tools
Beschreibung
Process mining is a family of data analysis methods that aims to discover, monitor, and improve organizational processes by analyzing data from so-called event logs. These event logs are generated by various information systems that are used in an organization and, therefore, capture how organizational processes are actually executed. The main difference to traditional data analysis techniques is that process mining explicitly focuses on the process perspective. That is, it aims to reveal the complex order relations among the activities captured in the event log. This lecture gives an introduction into the field of process mining. After introducing basic formalisms, the lecture provides a detailed and algorithmic perspective on the three key process mining technologies: process discovery conformance checking, and enhancement. What is more, it shows how process mining is currently used in industrial environments. The core idea of the lecture is to combine lectures and exercises such that students can directly apply theoretical concepts in the context of small assignments.
Please refer to the Moodle page for more details, lecture materials, and links to the online sessions: https://moodle2.uni-potsdam.de/course/view.php?id=35471
Voraussetzungen
- Completed Bachelor's studies.
- Recommended: Process-oriented Information Systems.
Literatur
- Wil van der Aalst: Process Mining: Discovery, Conformance, and Enhancement of Business Processes, Springer 2011
- Josep Carmona et al.: Conformance Checking Relating Processes and Models, Springer 2018.
- Mathias Weske: Business Process Management: Concepts, Languages, Architectures, Springer 2007
- Marlon Dumas et al.: Fundamentals of Business Process Management, Springer 2013
Lern- und Lehrformen
Lecture and Exercises.
Whether the lectures will be held onside or online depends on the number of enrollments.
Leistungserfassung
Final Exam.
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