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.
- 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
Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration (working title, preliminary)
(Own Funds)Term: since 2. January 2020
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.
- 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://dx.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 Proceedings of the Joint International Workshop on Big Data Management on Emerging Hardware and Data Management on Virtualized Active Systems held in Conjunction with ICDE 2021. Chania, Crete, Greece.
- 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://dx.doi.org/10.1007/s13222-021-00385-9
- 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.,... Mayer, F. (2020). Thinking small: next-generation sensor networks close the size gap in vertebrate biologging. Plos Biology.
- 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 Proc. Conf. "Lernen, Wissen, Daten, Analysen" (pp. 270 - 278). Bonn / Online, DE: CEUR-WS.