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• W1856102511 abstract "The Semantic Web comprises enormous volumesof semi-structured data elements. For interoperability, theseelements are represented by long strings. Such representationsare not efficient for the purposes of Semantic Web applicationsthat perform computations over large volumes of information.A typical method for alleviating the impact of this problem isthrough the use of compression methods that produce morecompact representations of the data. The use of dictionaryencoding for this purpose is particularly prevalent in SemanticWeb database systems. However, centralized implementationspresent performance bottlenecks, giving rise to the need forscalable, efficient distributed encoding schemes. In this paper,we describe an encoding implementation based on the asynchronouspartitioned global address space (APGAS) parallelprogramming model. We evaluate performance on a cluster ofup to 384 cores and datasets of up to 11 billion triples (1.9TB). Compared to the state-of-art MapReduce algorithm, wedemonstrate a speedup of 2:6 - 7:4X and excellent scalability.These results illustrate the strong potential of the APGASmodel for efficient implementation of dictionary encoding andcontributes to the engineering of larger scale Semantic Webapplications." @default.
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• W1856102511 date "2014-03-10" @default.
• W1856102511 modified "2023-09-23" @default.
• W1856102511 title "Scalable RDF Data Compression using X10" @default.
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