http://dl.gi.de/handle/20.500.12116/10989 Thu, 23 Jul 2026 09:11:34 GMT 2026-07-23T09:11:34Z http://dl.gi.de/handle/20.500.12116/11023 Einsatz eines Datenstrommanagementsystems als Framework für Online-Recommender-Systeme am Beispiel der Nachrichtenempfehlungen Ludmann, Cornelius A. Im Rahmen der CLEF NewsREEL Challenge haben Teilnehmerinnen und Teilnehmer die Möglichkeit, Recommender-Systeme im Live-Betrieb für die Empfehlung von Nachrichtenartikeln zu evaluieren und sich mit anderen zu messen. Dazu werden sie durch Events über Impressions informiert und bekommen Requests, auf die sie mit Empfehlungen antworten müssen. Diese werden anschließend den Benutzerinnen und Benutzern angezeigt. Die Veranstalter messen, wie viele Empfehlungen tatsächlich angeklickt werden.Eine Herausforderung ist die zeitnahe Verarbeitung der Events, um in einem festgelegten Zeitraum mit Empfehlungen antworten zu können. In diesem Beitrag stellen wir unseren Ansatz auf Basis des Datenstrommanagementsystems »Odysseus« vor, mit dem wir durch kontinuierlich laufende Queries beliebte Nachrichtenartikel empfehlen. Mit diesem konnten wir uns im Rahmen der CLEF NewsREEL Challenge 2016 gegenüber den anderen Teilnehmern behaupten und die meisten Klicks auf unsere Empfehlungen erzielen. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11023 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11021 Preserving Recomputability of Results from Big Data Transformation Workflows Kricke, Matthias; Grimmer, Martin; Schmeißer, Michael The ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. However, data transformation processes are heterogeneous and it is possible that manual work of domain experts is part of the process to create a deliverable data product. Domain experts and their work are expensive and time consuming, a recomputation process needs the ability of automatically adding former human interactions. It becomes even more challenging when external systems are used or data changes over time. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores. Furthermore, the system architecture will contain the possibility of incorporating human interactions of former data transformation processes. We will describe how our approach significantly relieves external systems and at the same time increases the performance of the big data transformation workflows. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11021 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11022 The First Data Science Challenge at BTW 2017 Hirmer, Pascal; Waizenegger, Tim; Falazi, Ghareeb; Abdo, Majd; Volga, Yuliya; Askinadze, Alexander; Liebeck, Matthias; Conrad, Stefan; Hildebrandt, Tobias; Indiono, Conrad; Rinderle-Ma, Stefanie; Grimmer, Martin; Kricke, Matthias; Peukert, Eric The 17th Conference on Database Systems for Business, Technology, and Web (BTW2017) of the German Informatics Society (GI) took place in March 2017 at the University of Stuttgart in Germany. A Data Science Challenge was organized for the first time at a BTW conference by the University of Stuttgart and Sponsor IBM. We challenged the participants to solve a data analysis task within one month and present their results at the BTW. In this article, we give an overview of the organizational process surrounding the Challenge, and introduce the task that the participants had to solve. In the subsequent sections, the final four competitor groups describe their approaches and results. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11022 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11020 Data Lakes Mathis, Christian By moving data into a centralized, scalable storage location inside an organization – the data lake – companies and other institutions aim to discover new information and to generate value from the data. The data lake can help to overcome organizational boundaries and system complexity. However, to generate value from the data, additional techniques, tools, and processes need to be established which help to overcome data integration and other challenges around this approach. Although there is a certain agreed-on notion of the central idea, there is no accepted definition what components or functionality a data lake has or how an architecture looks like. Throughout this article, we will start with the central idea and discuss various related aspects and technologies. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11020 2017-01-01T00:00:00Z