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SparkRDF: In-Memory Distributed RDF Management Framework for Large-Scale Social Data

  • Zhichao Xu
  • Wei ChenEmail author
  • Lei Gai
  • Tengjiao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Considering the scalability and semantic requirements, Resource Description Framework (RDF) and the de-facto query language SPARQL are well suited for managing and querying online social network (OSN) data. Despite some existing works have introduced distributed framework for querying large-scale data, how to improve online query performance is still a challenging task. To address this problem, this paper proposes a scalable RDF data framework, which uses key-value store for offline RDF storage and pipelined in-memory based query strategy. The proposed framework efficiently supports SPARQL Basic Graph Pattern (BGP) queries on large-scale datasets. Experiments on the benchmark dataset demonstrate that the online SPARQL query performance of our framework outperforms existing distributed RDF solutions.

Keywords

RDF SPARQL Social networks Query processing 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhichao Xu
    • 1
    • 2
  • Wei Chen
    • 1
    • 2
    Email author
  • Lei Gai
    • 1
    • 2
  • Tengjiao Wang
    • 1
    • 2
  1. 1.Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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