Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration (working title, preliminary)
Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration (working title, preliminary)
(Own Funds)
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Start date: 02/01/2020
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Acronym: ANANIA
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Abstract
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.