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Chair of Computer Science 6

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  4. 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)

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  • Evolutionary Information Systems
    • Sprechaktbasiertes Fallmanagement
    • Open and Collaborative Query-Driven Analytics
    • Processing Heterogeneous Assets and Resources to discover Ontologies and Semantics
    • Schema Inference and Machine Learning
  • Data Quality
    • DQ-Step – Verbesserung der Datenqualität bei AREVA NP / Abteilung NEM-G
    • Data quality and innovative capability of medical products
  • Data Integration
    • DQ-Step – Verbesserung der Datenqualität bei AREVA NP / Abteilung NEM-G
    • Open and Collaborative Query-Driven Analytics
    • Processing Heterogeneous Assets and Resources to discover Ontologies and Semantics
    • Schema Inference and Machine Learning
  • Process Management
    • Sprechaktbasiertes Fallmanagement
  • Database Systems
    • REAPER: A Framework for Materializing and Reusing Deep-Learning Models
    • Data Stream Application Manager
    • Know Your Queries!
    • Assessment of Data Management Systems
    • Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis
    • Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis (Phase II)
    • Schema Inference and Machine Learning
    • Architecture of Non-Multiple Autoencoders for Non-Lossy Information Agglomeration (working title, preliminary)
  • Datastream Systems
    • Data Stream Application Manager
    • Cross-system Optimization of Data-stream Queries
  • Data Management in the Digital Humanities
    • Franken in historischen Reiseberichten
    • Campusnetzwerk Digitale Geistes- und Sozialwissenschaften
  • Modern Database Systems

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)

Overall project:
Project leader: Klaus Meyer-Wegener
Project members: Dominik Probst
Start date: 02/01/2020
End date:
Acronym: ANANIA
Funding source:
URL:

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.

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Chair of Computer Science 6 (Data Management)
Friedrich-Alexander-Universität Erlangen-Nürnberg

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