DP

Chair of Computer Science 6 (Data Management)

Research associates

Address

Martensstraße 391058 Erlangen

Room: 08.157, Floor: 08

Contact

Since January 2020, Dominik Probst is a member of the research staff at our chair.

 

Background at the Chair

From October 2014 to September 2019 he was a tutor in “Konzeptioneller Modellierung” (eight semesters) and our part of the “Fertigungstechnisches Praktikum” (in SS2016 and SS2017).

 

Research

His research primarily focuses on compression techniques in databases. In addition, he is involved in research activities aimed at improving teaching at the chair.

Associated projects and publications can be accessed via the FAU’s Current Research Information System (CRIS):

  • Generation of Symbol Tables for String Compression with Frequent-Substring Trees

    (Own Funds)

    Term: since 19/09/2022
    Abstract
    With the ongoing rise in global data volumes, database compression is becoming increasingly relevant. While the compression of numeric data types has been extensively researched, the compression of strings has only recently received renewed scientific attention.A promising approach to string compression is the use of symbol tables, where recurring substrings within a database are substituted with short codes. A corresponding table enables the smooth reconstruction of the original data. This method is distinguished by short compression and decompression times, although the compression rate heavily depends on the quality of the symbol table.The research project FST focuses on the creation of optimized symbol tables to maximize the compression rate. For this purpose the eponymous Frequent-Substring Trees are constructed, a trie-like data structure that maps all potential table entries and enables the identification of optimal entries through the use of metadata.The primary objective of the research project is to increase the compression rate of string compression methods without significantly affecting the compression and decompression times.

  • Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration (working title, preliminary)

    (Own Funds)

    Term: 02/01/2020 – 19/09/2022
    Abstract
    The compression of data has played a decisive role in data management for a long time. Compressed data can be permanently stored in a more space-saving manner and sent over the network more efficiently. However, the ever-increasing volumes of data mean that the importance of good compression methods is growing all the time.Within the scope of project Anania (Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration), we are investigating to what extent classical compression methods in relational databases can be supplemented and improved using methods from machine learning.The project focuses on autoencoders that can recognize semantic connections in relations when applied tuple-wise and thus promise further improvement in the compression of relational data. Combinations of autoencoders and classical compression methods are also a possible focus of the project.Side note: The name of the project “Anania” was chosen in reference to the butterfly “Anania funebris”. In its stylized form, an autoencoder strongly resembles the silhouette of a butterfly with outstretched wings, which made the choice of this acronym seem fitting.

2025

2021

 

Teaching

His teaching responsibilities as a research assistant include, among others:

  • Supervision of seminar papers, project work, and theses (associated courses: NTDM and BDSem)
  • Exercise supervision and course organization for the subject “Process-Oriented Information Systems” (topics include BPMN, business process modeling, and process-driven architectures) in SS2020 and SS2021
  • Co-lecturer (SS2022 and SS2023) and sole lecturer (SS2024 and SS2025) for the course “Knowledge Discovery in Databases” (topic: data mining with excursions into OLAP), with 400+ registered students per semester (link to course materials: https://fau-cs6.github.io/KDD/)

Consultation hours can be arranged in writing via email.

His current teaching assignments can be viewed via Campo: