Instructors
Prof. Dr. Tilmann Rabl , Prof. Dr. Felix Naumann
Description
This all-to-all collaborative lecture series will feature presentations by distinguished researchers from German universities on the topic of database research. Besides an overview of topics in the field, we will introduce excellent database research groups in Germany. The lecture series is a collaboration of many database research groups and will be offered at multiple universities in parallel. The format will be hybrid with local and remote presentations and a parallel student poster session at all participating universities - a national DB event.
Recordings of the presentations will be available on Tele-Task.
Agenda
The course will be held in L-E.03, Tuesdays, 5pm.
| Date | Topic | Lecturer |
| 17.10.2023 | Opening | Tilmann Rabl (HPI) |
| 24.10.2023 | TBD | Pınar Tözün (IT University of Copenhagen) |
| 31.10.2023 | No course | |
| 07.11.2023 | System Infrastructure for Data-centric ML Pipelines | Matthias Boehm (TU Berlin) |
| 14.11.2023 | A Fix for the Fixation on Fixpoints (Rethinking Iteration in SQL) | Torsten Grust (University of Tübingen) |
| 21.11.2023 | TBD | Hannes Mühleisen (CWI) |
| 28.11.2023 | Challenges with JSON Schema Data Modeling | Stefanie Scherzinger (University of Passau) |
| 05.12.2023 | Commoditizing Data Analytics in the Cloud | Viktor Leis (TU Munich) |
| 12.12.2023 | Query Processing on Disaggregated Systems: Open Problems and Some Solutions | Wolfgang Lehner (TU Dresden) |
| 19.12.2023 | TBD | Zsolt Istvan (TU Darmstadt) |
| 26.12.2023 | Winter break | |
| 02.01.2024 | Winter break | |
| 09.01.2024 | Pushing Computation to the Sources: On Distribution in Complex Event Processing | Matthias Weidlich (HU Berlin) |
| 16.01.2024 | Data Cleaning | Ziawasch Abedjan (Leibniz Universität Hannover) |
| 23.01.2024 | Data Profiling | Felix Naumann (HPI) |
| 30.01.2024 | Towards Learned Database Systems | Carsten Binnig (TU Darmstadt) |
| 06.02.2024 | TBD | Jana Giceva (TU Munich) |
| 09.02.2024 | Student Poster Session (L-E-03) | |
General information
Time and Location: The presentations will be held in a hybrid fashion. Some of them may be conducted fully online, which we will announce beforehand.
You can access the course on Moodle.
The course is graded and has 3 ECTS.
Deliverables
Lecture summary
You have to write a summary for one lecture. Depending on the number of participants, this will be done in groups. Each lecture will be assigned to one person or group.
All summaries will be published on the course website. The summary should give a quick and engaging overview of the talk (10-15 min reading time). We will ask the presenters to review your summary and help you with questions.
Individual Poster Project
You have to prepare an A1 poster, with one of the following contents depending on your study level. You will be able to present your poster at the final session.
Bachelor Students: Based on one or more of the lectures, you can either prepare a technology landscape or explain a data science or data engineering process. Both should be as detailed as possible.
Master Students: To extend technology or methodology presented in one or multiple of the lectures, you should prepare a research project proposal. The poster should highlight goal, problem, and solution of your proposal and the connection to the lecture.
Mark Breakdown
- 50% - Lecture summary
- 50% - Individual poster project
