Cited By
View all- Fedoryszak MTkaczyk DBolikowski Ł(2013)Large Scale Citation Matching Using Apache HadoopResearch and Advanced Technology for Digital Libraries10.1007/978-3-642-40501-3_37(362-365)Online publication date: 2013
Entity matching for semistructured data in the Cloud
Authors: 
Marcus Paradies, Susan Malaika, Jérôme Siméon, Shahan Khatchadourian, Kai-Uwe SattlerAuthors Info & Claims
Pages 453 - 458
Abstract
The rapid expansion of available information, on the Web or inside companies, is increasing. With Cloud infrastructure maturing (including tools for parallel data processing, text analytics, clustering, etc.), there is more interest in integrating data to produce higher-value content. New challenges, notably include entity matching over large volumes of heterogeneous data.
In this paper, we describe an approach for entity matching over large amounts of semistructured data in the Cloud. The approach combines ChuQL[4], a recently proposed extension of XQuery with MapReduce, and a blocking technique for entity matching which can be efficiently executed on top of MapReduce. We illustrate the proposed approach by applying it to extract automatically and enrich references in Wikipedia and report on an experimental evaluation of the approach.
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S. Khatchadourian, M. Consens, and J. Simeon. Having a ChuQL at XML on the Cloud. In AMW, 2011.
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S. Khatchadourian, M. Consens, and J. Simeon. ChuQL: Processing XML with XQery using Hadoop. In Cascon, 2011.
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Published: 26 March 2012
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View all- Fedoryszak MTkaczyk DBolikowski Ł(2013)Large Scale Citation Matching Using Apache HadoopResearch and Advanced Technology for Digital Libraries10.1007/978-3-642-40501-3_37(362-365)Online publication date: 2013
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