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Auflistung nach Autor:in "Schenkel, Ralf"

1 - 10 von 13
  • Zeitschriftenartikel
    „Data Engineering“ in der Hochschullehre
    (Datenbank-Spektrum: Vol. 21, No. 3, 2021) Schenkel, Ralf; Scherzinger, Stefanie; Tropmann-Frick, Marina
    Das Themenheft zu „Data Engineering for Data Science“ gibt uns Anlass, die Rolle dieses Themas in der akademischen Datenbanklehre im Rahmen einer kleinen Umfrage zu erfassen. In diesem Artikel geben wir die Ergebnisse gesammelt wieder. Uns haben 17 Rückmeldungen aus der GI-Fachgruppe Datenbanksysteme erreicht. Im Vergleich zu einer früheren Umfrage zur Lehre im Bereich „Cloud“, 2014 im Datenbankspektrum vorgestellt, zeichnet sich ab, dass Data-Engineering-Inhalte zunehmend auch in grundständigen Lehrveranstaltungen gelehrt werden, sowie außerhalb der Kerninformatik. Data Engineering scheint sich als ein Querschnittsthema zu etablieren, das nicht nur den Masterstudiengängen vorbehalten ist.
  • Zeitschriftenartikel
    Editorial
    (Datenbank-Spektrum: Vol. 10, No. 1, 2010) Schenkel, Ralf; Stein, Benno
  • Zeitschriftenartikel
    Editorial
    (Datenbank-Spektrum: Vol. 21, No. 3, 2021) Schenkel, Ralf; Scherzinger, Stefanie; Tropmann-Frick, Marina; Härder, Theo
  • Zeitschriftenartikel
    Editorial
    (Datenbank-Spektrum: Vol. 14, No. 1, 2014) Schenkel, Ralf; Womser-Hacker, Christa
  • Journal
    Editorial
    (Datenbank-Spektrum: Vol. 18, No. 2, 2018) Michel, Sebastian; Gemulla, Rainer; Schenkel, Ralf; Härder, Theo
  • Zeitschriftenartikel
    Editorial
    (Datenbank-Spektrum: Vol. 11, No. 1, 2011) Härder, Theo; Schenkel, Ralf
  • Zeitschriftenartikel
    News
    (Datenbank-Spektrum: Vol. 16, No. 1, 2016) Schenkel, Ralf
  • Zeitschriftenartikel
    News
    (Datenbank-Spektrum: Vol. 16, No. 2, 2016) Schenkel, Ralf
  • Konferenzbeitrag
    Sharing knowledge between independent grid communities
    (INFORMATIK 2011 – Informatik schafft Communities, 2011) Hose, Katja; Metzger, Steffen; Schenkel, Ralf
    In recent years, grid-based approaches for processing scientific data became popular in various fields of research. A multitude of communities has emerged that all benefit from the processing and storage power the grid offers to them. So far there has not yet been much collaboration between these independent communities. But applying semantic technologies to create knowledge bases, sharing this knowledge, and providing access to data maintained by a community, allows to exploit a synergy effect that all communities can benefit from. In this paper, we propose a framework that applies information extraction to generate abstract knowledge from source documents to be shared among participating communities. The framework also enables users to search for documents based on keywords or metadata as well as to search for extracted knowledge. This search is not restricted to the community the user is registered at but covers all registered communities and the data they are willing to share with others.
  • Zeitschriftenartikel
    The ReCAP Project
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Bergmann, Ralph; Biertz, Manuel; Dumani, Lorik; Lenz, Mirko; Ludwig, Anna-Katharina; Neumann, Patrick J.; Ollinger, Stefan; Sahitaj, Premtim; Schenkel, Ralf; Witry, Alex
    Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.