Universität Rostock, Lehrstuhl Datenbank- und Informationssysteme: Dbis Repository
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Intra-Life-Learning mittels parallelisierter Neuroevolution.
No full textIm Bereich des maschinellen Lernens wird bei dem Training von neuronalen Netzen üblicherweise zwischen der Trainings- und Einsatzphase unterschieden. Ändert sich jedoch die Datenbasis bezüglich einer Domäne, so muss die Trainingsphase für ein neuronales Netz komplett neu durchgeführt werden und schon erlerntes Wissen wird dabei komplett ignoriert. Diese Arbeit beschäftigt sich mit alternativen Lernverfahren, wobei das Ziel darin besteht, das Lernen eines neuronalen Netzes effizienter bezüglich unterschiedlicher Parameter wie z. B. Trainingszeit oder benötigte Trainingsbeispiele zu gestalten
Auswirkungen der EU-DSGVO auf das Online-Marketing – Eine Analyse am Beispiel von datengetriebenen Dienstleistungsunternehmen
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Datenbanksysteme für Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 4.-8. März 2019, Rostock, Germany, Proceedings
No full textParticulate Matter Matters---The Data Science Challenge @ BTW 2019
No full textFor the second time, the Data Science Challenge took place as part of the 18th symposium ``Database Systems for Business, Technology and Web'' (BTW) of the Gesellschaft für Informatik (GI). The Challenge was organized by the University of Rostock and sponsored by IBM and SAP. This year, the integration, analysis and visualization around the topic of particulate matter pollution was the focus of the challenge. After a preselection round, the accepted participants had one month to adapt their developed approach to a substantiated problem, the real challenge. The final presentation took place at BTW 2019 in front of the prize jury and the attending audience. In this article, we give a brief overview of the schedule and the organization of the Data Science Challenge. In addition, the problem to be solved and its solution will be presented by the participants
Calculating Fourier Transforms in SQL
No full textThe Fourier transform is an important tool for analyzing, transforming and searching multi-media content in databases. SQL is the lingua franca for querying structured data. Implementing the Discrete Fourier Transform (DFT) in SQL itself has several benefits. The DFT can directly be executed in the database system. It can be reused for several, different content processing steps from feature extraction to query transformation and evaluation.
We not only discuss different algorithmic aspects but also do a performance evaluation on top of different database systems of different architectures, i.e. row and column stores. The SQL-based implementation is also compared to a Python-based implementation on the client side. There is no variant that always performs best