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Matches in ScholarlyData for { ?s ?p ?o. }

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• item-2 hasContent birte-glimm.
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• item-3 hasContent ian-horrocks.
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• 1 creator carlos-buil-aranda.
• 1 creator marcelo-arenas.
• 1 creator oscar-corcho.
• 1 type InProceedings.
• 1 label "Semantics and optimization of the SPARQL 1.1 federation extension".
• 1 sameAs 1.
• 1 abstract "The W3C SPARQL working group is defining the new SPARQL 1.1 query language. The current working draft of SPARQL 1.1 focuses mainly on the description of the language. In this paper, we provide a formalization of the syntax and semantics of the SPARQL 1.1 federation extension, an important fragment of the language that has not yet received much attention. Besides, we propose optimization techniques for this fragment, provide an implementation of the fragment including these techniques, and carry out a series of experiments that show that our optimization procedures could significantly speed up the query evaluation process.".
• 1 hasAuthorList authorList.
• 1 isPartOf proceedings.
• 1 keyword "SPARQL 1.1".
• 1 keyword "distributed query processing".
• 1 keyword "query optimization".
• 1 title "Semantics and optimization of the SPARQL 1.1 federation extension".
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• item-1 hasContent carlos-buil-aranda.
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• item-2 hasContent oscar-corcho.
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• item-3 type ListItem.
• item-3 hasContent marcelo-arenas.
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• 13 creator hyeongsik-kim.
• 13 creator kemafor-anyanwu.
• 13 creator padmashree-ravindra.
• 13 type InProceedings.
• 13 label "Nested TripleGroup Algebra – An Intermediate Algebra for Optimizing RDF Graph Pattern Matching on MapReduce Platforms".
• 13 sameAs 13.
• 13 abstract "Processing a join operation using MapReduce platforms such as Hadoop incurs communication and I/O costs due to data transfer between the Map and Reduce phases. This cost is prohibitive for RDF graph patterns which typically involve several joins. Existing approaches on optimizing RDF graph pattern matching exploit the existence of star-join structures in RDF graph patterns and propose the use of bushy query execution plans instead of linear ones. However, some Hadoop-based data processing systems such as Yahoo’s Pig only support linear execution plans, and require significant modifications to the scheduler to support bushy plans. In this paper, we propose an approach for “sneaking in” bushy query execution plans into Hadoop by interpreting star joins as “groups of triples” known as TripleGroups. We present an alternative intermediate TripleGroup-based algebra called the Nested Triple Group Algebra (NTGA) for rewriting star-join queries as TripleGroup queries. We also propose a data representation format (RDFMap) that more efficiently supports TripleGroup-based processing than the existing relational tuple infrastructure. We present a comparative performance evaluation of the traditional Pig approach and RAPID+ (Pig extended with NTGA) for graph pattern matching queries on the BSBM benchmark dataset. Results show over 60% performance of our approach over traditional Pig for some tasks.".
• 13 hasAuthorList authorList.
• 13 isPartOf proceedings.
• 13 keyword "MapReduce".
• 13 keyword "Optimization Techniques".
• 13 keyword "RDF Graph Pattern Matching".
• 13 title "Nested TripleGroup Algebra – An Intermediate Algebra for Optimizing RDF Graph Pattern Matching on MapReduce Platforms".
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• item-1 type ListItem.
• item-1 hasContent padmashree-ravindra.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent hyeongsik-kim.
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• item-3 type ListItem.
• item-3 hasContent kemafor-anyanwu.
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• 23 creator gerhard-weikum.
• 23 creator maya-ramanath.
• 23 creator shady-elbassuoni.
• 23 type InProceedings.
• 23 label "Query Relaxation for Entity-Relationship Search".
• 23 sameAs 23.
• 23 abstract "Entity-relationship-structured data is becoming more important on the Web. For example, large knowledge bases have been automatically constructed by information extraction from Wikipedia and other Web sources. Entities and relationships can be represented by subject-property-object triples in the RDF model, and can then be precisely searched by structured query languages like SPARQL. Because of their Boolean-match semantics, such queries often return too few or even no results. To improve recall, it is thus desirable to support users by automatically relaxing or reformulating queries in such a way that the intention of the original user query is preserved while returning a sufficient number of ranked results. In this paper we describe comprehensive methods to relax SPARQL-like triple-pattern queries in a fully automated manner. Our framework produces a set of relaxations by means of statistical language models for structured RDF data and queries. The query processing algorithms merge the results of different relaxations into a unified result list, with ranking based on any ranking function for structured queries over RDF-data. Our experimental evaluation, with two different datasets about movies and books, shows the effectiveness of the automatically generated relaxations and the improved quality of query results based on assessments collected on the Amazon Mechanical Turk platform.".
• 23 hasAuthorList authorList.
• 23 isPartOf proceedings.
• 23 keyword "entity relationship".
• 23 keyword "rdf".
• 23 keyword "relaxation".
• 23 keyword "semantic search".
• 23 keyword "sparql".
• 23 title "Query Relaxation for Entity-Relationship Search".
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• item-1 type ListItem.
• item-1 hasContent shady-elbassuoni.
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• item-2 type ListItem.
• item-2 hasContent maya-ramanath.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent gerhard-weikum.
• item-3 previous item-2.
• 26 creator antonios-deligiannakis.
• 26 creator panos-constantopoulos.
• 26 creator vicky-dritsou.

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