P-LUPOSDATE: Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud

Sven Groppe, Thomas Kiencke, Stefan Werner, Dennis Heinrich, Marc Stelzner, Le Gruenwald

Abstract

Increasingly data on the Web is stored in the form of Semantic Web data. Because of today’s information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fashion. Cloud Computing offers such a distributed environment with dynamic reallocation of computing and storing resources based on needs. In this work we introduce a scalable distributed Semantic Web database in the Cloud. In order to reduce the number of (unnecessary) intermediate results early, we apply bloom filters. Instead of computing bloom filters, a time-consuming task during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even reduces the processing time to less than half.
OriginalspracheEnglisch
ZeitschriftOpen Journal of Semantic Web (OJSW)
Jahrgang1
Ausgabenummer2
Seiten (von - bis)25-55
Seitenumfang31
ISSN2199-336X
PublikationsstatusVeröffentlicht - 2014

Strategische Forschungsbereiche und Zentren

  • Querschnittsbereich: Intelligente Systeme
  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)

DFG-Fachsystematik

  • 409-06 Informationssysteme, Prozess- und Wissensmanagement
  • 4.43-03 Sicherheit und Verlässlichkeit, Betriebs-, Kommunikations- und verteilte Systeme

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