S2X: graph-parallel querying of RDF with GraphX

Abstract: RDF has constantly gained attention for data publishing due to its flexible data model, raising the need for distributed querying. However, existing approaches using general-purpose cluster frameworks employ a record-oriented perception of RDF ignoring its inherent graph-like structure. Recently, GraphX was published as a graph abstraction on top of Spark, an in-memory cluster computing system. It allows to seamlessly combine graph-parallel and data-parallel computation in a single system, an unique feature not available in other systems. In this paper we introduce S2X, a SPARQL query processor for Hadoop where we leverage this unified abstraction by implementing basic graph pattern matching of SPARQL as a graph-parallel task while other operators are implemented in a data-parallel manner. To the best of our knowledge, this is the first approach to combine graph-parallel and data-parallel computation for SPARQL querying of RDF data based on Hadoop

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Ausgabe
Postprint
Sprache
Englisch
Anmerkungen
Wang F., Luo G., Weng C., Khan A., Mitra P., Yu C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) 2015, DMAH 2015. Lecture Notes in Computer Science, vol 9579, isbn: 978-3-319-41575-8
cc_by_nc_nd http://creativecommons.org/licenses/by-nc-nd/4.0/deed.de cc

Klassifikation
Informatik
Schlagwort
Hadoop
RDF
SPARQL
Semantic Web

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2016
Urheber
Beteiligte Personen und Organisationen

DOI
10.1007/978-3-319-41576-5_12
URN
urn:nbn:de:bsz:25-freidok-122783
Rechteinformation
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Letzte Aktualisierung
14.08.2025, 10:44 MESZ

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Entstanden

  • 2016

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