Storing and Indexing Massive RDF Datasets

Luo, Yongming; Picalausa, François; Fletcher, George H. L.; Hidders, Jan; Vansummeren, Stijn

doi:10.1007/978-3-642-25008-8_2

Cited by 35 publications

(24 citation statements)

References 65 publications

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“…Various techniques for indexing linked data have been developed since the advent of the Semantic Web; and several surveys of these techniques have been presented [27]. In this work we divide RDF data indexing into four major categories.…”

Section: Indexingmentioning

confidence: 99%

A Taxonomy of Semantic Web Data Retrieval Techniques

Butt

Haller

Xie

2015

Proceedings of the 8th International Conference on Knowledge Capture

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The Semantic Web provides access to an increasing amount of structured information in a wide variety of domains. Information overload due to the large amount of structured data is as much a problem as on the traditional Web. To solve this problem, ample research has been proposed on Semantic Web data retrieval techniques and after more than a decade of research in this domain it is now reasonable to consider the questions: is the field of Semantic Web data retrieval making progress? What are the directions that have been taken? and what are some of the promising significant directions to pursue future research? To answer these questions, we review the state-of-the-art Semantic Web data retrieval techniques and define a taxonomy of these techniques to classify the ongoing research and find potential future research directions.

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Section: Indexingmentioning

confidence: 99%

A Taxonomy of Semantic Web Data Retrieval Techniques

Butt

Haller

Xie

2015

Proceedings of the 8th International Conference on Knowledge Capture

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“…Hive, for example, allows storing data in HDFS based on a relational schema that defined by the user. Though there are some discrepancies among researchers regarding the naming and classification of relational schemas for RDF data, most researchers classify these schemas in to three groups (Abadi et al, 2007;Harth, Hose, & Schenkel, 2014;Luo et al, 2012;Sakr & Al-Naymat, 2010): …”

Section: Rdf Storesmentioning

confidence: 99%

“…For the Semantic Web to work, both triple-stores and SPARQL query processing engines have to scale well with the size of data. This is especially true when the size of RDF data is too big such that it is difficult, if not impossible, for conventional triple-stores to work with (Cudré-Mauroux et al, 2013;Luo, Picalausa, Fletcher, Hidders, & Vansummeren, 2012;Wilkinson, Sayers, Kuno, Reynolds, & others, 2003) In the past few years, however, new advances have been made in the processing of large volumes of data sets, aka big data, which can be used for processing big RDF data (Abadi, Marcus, Madden, & Hollenbach, 2007;Morsey, Lehmann, Auer, & Ngomo, 2011;Sakr & AlNaymat, 2010).…”

Section: Introductionmentioning

confidence: 99%

Distributed SPARQL Querying Over Big RDF Data Using Presto-RDF

Mammo¹,

Hassan²,

Bansal³

2015

STBD

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The processing of large volumes of RDF data requires an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational database management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. This paper proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data. We evaluate the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done with RDF datasets of size 10, 20, and 30 million triples. The results of the experiments show that Presto-RDF has a much higher performance than Hive and can be used to process big RDF data.

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“…Several approaches have been proposed for storing both ontologies and ontology individuals in a database, to get benefit of the functionalities offered by DBMSs (query performance, data storage, transaction management, etc.) [24], [25], [59], [1], [2], [61], [16], [32], [36], [63], [80], [73], [81], [58], [51], [52], [33], [101], [42], [62], [100].…”

Section: Existing Contributionsmentioning

confidence: 99%