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공공누리This item is licensed Korea Open Government License

Title
Distributed RDF store for efficient searching billions of triples based on Hadoop
Author(s)
엄정호정창후김태홍송사광이승우정한민
Publication Year
2016-02-16
Abstract
As the development of IT and scientific technology, very large amountsof knowledge data are continuously being created and the big data era can be saidto have arrived. Therefore, RDF store inserting and inquiring into knowledge baseshas to be scaled up in order to deal with such large sources of data. To this end, wepropose a scalable distributed RDF store based on a distributed database that usesbulk-loading for billions of triples to store data and to respond to user queries quickly.In order to achieve this purpose, we introduce a bulk-loading algorithm using theMapReduce framework and the SPARQL query processing engine to connect to alarge distributed database. Experimental results show that the proposed bulk-loadingalgorithm achieves 67.893K triples per second to load approximately 33 billion triples.Therefore, the experiment proves proposed RDF store can manage billions of triplesscale data.
Keyword
Triple Store; Hbase; MapReduce; RDF; LOD
Journal Title
Journal of Supercomputing
Citation Volume
72
ISSN
0920-8542
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14514
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART75711639
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