Map-side merge joins for scalable SPARQL BGP processing

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Abstract: In recent times, it has been widely recognized that, due to their inherent scalability, frameworks based on MapReduce are indispensable for so-called "Big Data" applications. However, for Semantic Web applications using SPARQL, there is still a demand for sophisticated MapReduce join techniques for processing basic graph patterns, which are at the core of SPARQL. Renowned for their stable and efficient performance, sort-merge joins have become widely used in DBMSs. In this paper, we demonstrate the adaptation of merge joins for SPARQL BGP processing with MapReduce. Our technique supports both n-way joins and sequences of join operations by applying merge joins within the map phase of MapReduce while the reduce phase is only used to fulfill the preconditions of a subsequent join iteration. Our experiments with the LUBM benchmark show an average performance benefit between 15% and 48% compared to other MapReduce based approaches while at the same time scaling linearly with the RDF dataset size

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom), 2013 : 2 - 5 Dec. 2013, Bristol, United Kingdom ; [including workshops] / sponsors: IEEE Computer Society .... Piscataway, NJ: IEEE, 2013, Seite 631-749. DOI 10.1109/CloudCom.2013.9, isbn: 978-0-7695-5095-4
cc_by_nc_nd http://creativecommons.org/licenses/by-nc-nd/4.0/deed.de cc

Klassifikation
Informatik
Schlagwort
Hadoop
RDF
Semantic Web
SPARQL

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2013
Urheber
Beteiligte Personen und Organisationen
Technische Fakultät
Institut für Informatik
Albert-Ludwigs-Universität Freiburg

DOI
10.1109/CloudCom.2013.9
URN
urn:nbn:de:bsz:25-freidok-122735
Rechteinformation
Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
25.03.2025, 13:50 MEZ

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Beteiligte

Entstanden

  • 2013

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