Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6548
Title: Distributed RDFS Reasoning with MapReduce
Authors: Çetin, Yiğit
Abul, Osman
Keywords: Big data
Mapreduce
Hadoop
Rdfs reasoning
Issue Date: 2014
Publisher: Springer-Verlag Berlin
Source: 29th Annual Symposium on Computer and Information Sciences -- OCT 27-28, 2014 -- Krakow, POLAND
Abstract: We live in big data age in which many computational tasks either generate or need to use large datasets. This makes parallel and distributed computing a key for scalability. MapReduce is a programming model for processing large datasets in parallel and distributed fashion on cluster of computers. Today, since the size and complexity of RDFS documents increase rapidly, RDFS reasoning problem has to embrace and address the big data solutions. The output of RDFS reasoning job can be input to another job and the output of RDFS reasoning jobs grow big as the input documents gets bigger. In this study, an indexing method is proposed to speed up the RDFS reasoning over Hadoop clusters. We also explore the utility of caching and Hadoop ecosystem tools Apache Hive and Apache Pig for this task. Experimental evaluations on Dbpedia and Freebase datasets show that the indexing method is quite effective and offers scalable solutions. Performance of caching and Apache Hive is found acceptable too.
URI: https://doi.org/10.1007/978-3-319-09465-6_32
https://hdl.handle.net/20.500.11851/6548
ISBN: 978-3-319-09465-6
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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