Ausgabe 3/2021
Data Engineering for Data Science
Inhalt (12 Artikel)
Editorial
Ralf Schenkel, Stefanie Scherzinger, Marina Tropmann-Frick, Theo Härder
Collecting and visualizing data lineage of Spark jobs
Alexander Schoenenwald, Simon Kern, Josef Viehhauser, Johannes Schildgen
Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance
Peter K. Schwab, Jonas Röckl, Maximilian S. Langohr, Klaus Meyer-Wegener
Continuous Training and Deployment of Deep Learning Models
Ioannis Prapas, Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Volker Markl
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters
Daniyal Kazempour, Johannes Winter, Peer Kröger, Thomas Seidl
Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching
Lars Kegel, Claudio Hartmann, Maik Thiele, Wolfgang Lehner
Feature Engineering Techniques and Spatio-Temporal Data Processing
Chris-Marian Forke, Marina Tropmann-Frick
Kurz erklärt: Measuring Data Changes in Data Engineering and their Impact on Explainability and Algorithm Fairness
Meike Klettke, Adrian Lutsch, Uta Störl
„Data Engineering“ in der Hochschullehre
Ralf Schenkel, Stefanie Scherzinger, Marina Tropmann-Frick
The Collaborative Research Center FONDA
Ulf Leser, Marcus Hilbrich, Claudia Draxl, Peter Eisert, Lars Grunske, Patrick Hostert, Dagmar Kainmüller, Odej Kao, Birte Kehr, Timo Kehrer, Christoph Koch, Volker Markl, Henning Meyerhenke, Tilmann Rabl, Alexander Reinefeld, Knut Reinert, Kerstin Ritter, Björn Scheuermann, Florian Schintke, Nicole Schweikardt, Matthias Weidlich