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

  • 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.

  • Campusnetzwerk Digitale Geistes- und Sozialwissenschaften

    (Third Party Funds Group – Overall project)

    Term: 1. November 2016 - 31. October 2021
    Funding source: Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst (ab 10/2013)
    URL: https://izdigital.fau.de/category/techne/

    Digitaler Campus Bayern

2021

2020

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