Recent Developments in DB&IS (Seminar)

In order to attend, students must follow the registration process outlined below. Each chosen student will be assigned a research paper that they must present in a 45-minute talk, followed by a Q&A session. The presentation and Q&A will be conducted exclusively in English. Finally, students are required to submit a brief 4-page report on the paper within two weeks of the presentation.

Registration Process

  • To register, download the JSON template, rename it to {yourmatriculationnumber}.json, fill the details, and send it via email under Contact.
  • We'll announce the kick-off meeting date once registration is complete. The meeting is mandatory and will cover organizational aspects.



  • First Meeting with Supervisor & Summary: 28-05-2024
  • No-left-todo slides: 09-07-2024
  • Seminar Presentation dates: tbd
  • Final report submission: tbd
  • Registration: 12-04-2024


  • 29/04/2024 - Kick-off meeting slides & summary template uploaded.
  • 22/04/2024 - Kick-off meeting announced (29.04.2024 09:30-10:30 AM).
  • 15/04/2024 - Papers assigned.
  • 07/03/2024 - Registration dates announced.


IDPaper TitleStudentLink
1Automatic Database Management System Tuning Through Large-scale Machine LearningYvonne Bötzel
2DB-BERT: A Database Tuning Tool that “Reads the Manual”Paul Ohler
3Cross-Modal Data Discovery over Structured and Unstructured Data LakesErik Schwede
4Selectivity Estimation for Queries Containing Predicates over Set-Valued AttributesJan Heidemann
5Panakos: Chasing the Tails for Multidimensional Data StreamsOmar Al-Kubati
6Sieve: A Learned Data-Skipping Index for Data AnalyticsManas Lalit Mahajan
7Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-StoresJan Karklins
8BtrBlocks: Efficient Columnar Compression for Data LakesJoseph Blessingh Israel
9Grouping Time Series for Efficient Columnar StorageSimon Eggers
10APEX: A High-Performance Learned Index on Persistent MemoryJonathan Püttmann
11Sample-Efficient Cardinality Estimation Using Geometric Deep LearningDaniel Fischer