Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Data Extraction for Processing Mining (Sommersemester 2020)

Dozent: Prof. Dr. Mathias Weske (Business Process Technology) , Simon Remy (Business Process Technology) , Dorina Bano (Business Process Technology)

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 06.04.2020 - 22.04.2020
  • Lehrform: Seminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 6

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • IT-Systems Engineering
    • HPI-ITSE-E Entwurf
  • IT-Systems Engineering
    • HPI-ITSE-K Konstruktion
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-K Konzepte und Methoden
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-T Techniken und Werkzeuge
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
Digital Health MA


Process Mining, as a data-driven research field, relies on the availability of data in the form of event logs. While the amount of produced data increased dramatically in the past years, there is a lack of techniques to make them accessible for process mining.

A common way to store and organize data are databases or for larger organizations data warehouses. During this seminar, you will get familiar with existing data extraction methods, to identify and extract relevant information from databases, and to transform them into event logs. 

In the first two weeks of the semester, you will choose your topic of interest. Depending on the group size, you will work in groups of at most two students and present your result to the whole group.


Necessary literature will be provided for each topic exclusively.


The module will be graded based on:

  1. a short presentation (15min talk) at the middle of the semester [20%]
  2. a final presentation (20min talk) at the end of the semester [30%]
  3. (demo) paper (~12 pages, LNCS style) on the scenario and its implementation [50%]


Meetings will take place by arrangement. However, the first meeting will take place in the week starting on the 27th of April. Details will be announced as soon as possible.