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)
14 Downloads (Pure)

Abstract

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
PublisherSpringer
Pages103-112
Number of pages10
ISBN (Print)978-3-642-53973-2
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event1st 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
Volume8163

Workshop

Workshop1st Workshop on Big Data Benchmarks 2012
Abbreviated titleWBDB
Country/TerritoryUnited States
CitySan Jose
Period8/05/129/05/12

Keywords

  • Linked data
  • Natural Language Processing (NLP)
  • Data warehousing
  • Big data
  • n/a OA procedure

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