Unleashing Semantics of Research Data

Florian Stegmaier, Christin Seifert, Roman Kern, Patrick Hoefler, Sebastian Bayerl, Michael Granitzer, Harald Kosch, Stefanie Lindstaedt, Belgin Mutlu, Vedran Sabol, Kai Schlegel, Stefan Zwicklbauer

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)
    1 Downloads (Pure)


    Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.
    Original languageEnglish
    Title of host publicationSpecifying Big Data Benchmarks
    Subtitle of host publicationFirst Workshop, WBDB 2012, San Jose, CA, USA, May 8-9, 2012, and Second Workshop, WBDB 2012, Pune, India, December 17-18, 2012, Revised Selected Papers
    EditorsTilmann Rabl, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobsen
    Number of pages10
    ISBN (Print)978-3-642-53973-2
    Publication statusPublished - 2014
    EventFirst Workshop on Big Data Benchmarks 2012 - San Jose, United States
    Duration: 8 May 20129 May 2012
    Conference number: 1

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Berlin Heidelberg


    ConferenceFirst Workshop on Big Data Benchmarks 2012
    Abbreviated titleWBDB 2012
    Country/TerritoryUnited States
    CitySan Jose


    • Linked Data
    • Natural Language Processing
    • Data Warehousing
    • Big Data


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