Prof. Dr. Klaus Meyer-Wegener
Chair of Computer Science
I work on query processing in databases, and the use of artificial intelligence to boost database performance. Regarding the application of databases, I investigate the potential of collecting the queries first and then derive the database design from them.
Research projects
- Query-driven database design, integration, and optimization
- Using modern hardware (FPGAs) to speed up database-query processing
- Investigate AI methods for the improvement of database technology (e.g. autoencoder)
- Build a repository of neural-ne
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SKYSHARK: SKYSHARK -Benchmarking Data Processing Systems Using Real-Time Flight Data
(Own Funds)
To test and evaluate a heterogeneous stream-processing system consisting of an FPGA-based systemon-chip and a host, we develop a benchmark called SKYSHARK. It uses real-world data from air-traffic control that is publicly available. These data are enhanced for the purpose of the benchmark without changing their characteristics. They are further enriched with aircraft and airport data. We define 14 queries with respect to the particular requirements of our system. They should be useful for other hardware-accelerated platforms as well. A first evaluation has been done using Apache Flink. We envision a great potential because of the flexibility of the approach.
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FST: Generation of Symbol Tables for String Compression with Frequent-Substring Trees
(Own Funds)
Project leader:
Term: 19. September 2022 - 31. December 2027
Acronym: FSTWith 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.
2025
- Hahn, T., Langohr, M., Becher, A., Beena Gopalakrishnan Nair, L., Meyer-Wegener, K., Teich, J., & Wildermann, S. (2025). ReProVide: Query Optimization and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis. In Scalable Data Management for Future Hardware. (pp. 171-197).
- Hahn, T., Langohr, M., Meißner, S., Döring, B., Wildermann, S., Meyer-Wegener, K., & Teich, J. (2025). ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis. In Proceedings of the 21st Conference on Database Systems for Business, Technology and Web (pp. 899-906). Bamberg, DE.
- Sigl, M., & Meyer-Wegener, K. (2025). Towards Learning to Rank Deep-Learning Models for Multivariate Time-Series Transfer Learning. In DEEM '25: Proceedings of the Workshop on Data Management for End-to-End Machine Learning (pp. 2). Berlin, DE: New York, NY: Association for Computing Machinery, Inc.
2023
- Langohr, M., Vogler, T., & Meyer-Wegener, K. (2023). SKYSHARK: A Benchmark with Real-world Data for Line-rate Stream Processing with FPGAs. In Leyer M, Wichmann J (Eds.), Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Marburg, Germany, October 9-11, 2023 (pp. 98--109). Marburg, DE: CEUR-WS.org.
- Rückert, C., Meyer-Wegener, K., Safferling, C., & Freiling, F. (2023). Messengerdienst-Nachrichten als Beweismittel im Strafverfahren – am Beispiel der Auswertung von WhatsApp-Chats. Juristische Rundschau, 2023, 366-378. https://doi.org/10.1515/juru-2023-2051
2022
- Benenson, Z., Freiling, F., & Meyer-Wegener, K. (2022). Soziotechnische Einflussfaktoren auf die "digitale Souveränität" des Individuums. In Glasze, Georg; Odzuck; Eva; Staples, Ronald (Hrg.), Was heißt digitale Souveränität? Diskurse, Praktiken und Voraussetzungen "individueller" und "staatlicher Souveränität" im digitalen Zeitalter. (S. 61 - 87). Bielefeld: transcript Verlag.
2021
- Beena Gopalakrishnan Nair, L., Becher, A., Wildermann, S., Meyer-Wegener, K., & Teich, J. (2021). Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System. Datenbank-Spektrum. https://doi.org/10.1007/s13222-020-00363-7
- Beena Gopalakrishnan Nair, L., & Meyer-Wegener, K. (2021). COPRAO: A Capability Aware Query Optimizer for reconfigurable Near Data Processors. In Proc. 37th IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2021, Chania, Greece, April 19-22, 2021 (pp. 54-59). Chania, Crete, Greece, GR: IEEE.
- Schwab, P., Röckl, J., Langohr, M., & Meyer-Wegener, K. (2021). Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance. Datenbank-Spektrum. https://doi.org/10.1007/s13222-021-00385-9
2020
- Beena Gopalakrishnan Nair, L., Becher, A., & Meyer-Wegener, K. (2020). The ReProVide Query-Sequence Optimization in a Hardware-Accelerated DBMS. In DaMoN '20: Proceedings of the 16th International Workshop on Data Management on New Hardware (pp. 1-3). Portland, Oregon USA: ACM Digital Library.
- Beena Gopalakrishnan Nair, L., Becher, A., Meyer-Wegener, K., Wildermann, S., & Teich, J. (2020). SQL Query Processing Using an Integrated FPGA-based Near-Data Accelerator in ReProVide. In Proceedings of EDBT (pp. 4). Copenhagen, DK.
- Ripperger, S., Carter, G., Page, R., Duda, N., Kölpin, A., Weigel, R.,... Kapitza, R. (2020). Thinking small: next-generation sensor networks close the size gap in vertebrate biologging. Plos Biology. https://doi.org/10.1371/journal.pbio.3000655
- Schwab, P., Langohr, M., & Meyer-Wegener, K. (2020). A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models. In ACM (Eds.), GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (pp. 10:1-10:5). Portland, OR, US: ACM.
- Schwab, P., Langohr, M., & Meyer-Wegener, K. (2020). We Know What You Did Last Session: Policy-Based Query Classification for Data-Privacy Compliance With the DataEconomist. In Association for Computing Machinery (Eds.), Proceedings of the SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management (pp. 30:1 - 30:4). Vienna, Virtual Conference, AT: New York, NY, United States: International Conference Proceeding Series (ICPS).
- Schwab, P., & Meyer-Wegener, K. (2020). Towards Evolutionary, Domain-Specific Query Classification Based on Policy Rules. In Daniel Trabold, Pascal Welke, Nico Piatkowski (Eds.), Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (pp. 291-295). Online, DE: CEUR-WS.
- Vöhringer, D., & Meyer-Wegener, K. (2020). Future Fetch: Towards a ticket-based data access for secondary storage in database systems. In Daniel Trabold, Pascal Welke, Nico Piatkowski (Eds.), Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (pp. 270-278). Virtual, Online, DE: CEUR-WS.