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2016 | OriginalPaper | Buchkapitel

TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs

verfasst von : Chantana Chantrapornchai, Chidchanok Choksuchat, Michael Haidl, Sergei Gorlatch

Erschienen in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

Resource Description Framework (RDF) is a commonly used format for semantic web processing. It basically contains strings representing terms and their relationships which can be queried or inferred. RDF is usually a large text file which contains many million relationships. In this work, we propose a framework, TripleID, for processing queries of large RDF data. The framework utilises Graphics Processing Units (GPUs) to search RDF relations. The RDF data is first transformed to the encoded form suitable for storing in the GPU memory. Then parallel threads on the GPU search the required data. We show in the experiments that one GPU on a personal desktop can handle 100 million triple relations, while a traditional RDF processing tool can process up to 10 million triples. Furthermore, we can query sample relations within 0.18 s with the GPU in 7 million triples, while the traditional tool takes at least 6 s for 1.8 million triples.

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Titel: TripleID: A Low-Overhead Representation and Querying Using GPU for Large RDFs
verfasst von: Chantana Chantrapornchai
Chidchanok Choksuchat
Michael Haidl
Sergei Gorlatch
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery
Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-34099-9_31

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