Matches in ScholarlyData for { ?s ?p ?o. }
- 49 type InProceedings.
- 49 label "Efficiently Evaluating Skyline Queries on RDF Databases".
- 49 sameAs 49.
- 49 abstract "Skyline queries are a class of preference queries that are valuable for multi-criteria decision making scenarios. Such queries compute the pareto-optimal tuples from a set of tuples. This problem has received significant attention in the context of relational data where many techniques focus on answering queries over a single table. Consequently, for multi-relational skyline query scenarios, as would be the norm for RDF, the strategy for query evaluation would need to be a join-first-skyline-later strategy. However, such a split computational strategy limits the optimization opportunities that are useful for pruning search space via information passing between the join phase and the skyline phase. Other available techniques for multi-relational skyline queries assume storage and indexing techniques that are not typically used with RDF, thereby requiring a preprocessing step. In this paper, we present an approach for optimizing skyline queries over RDF data. The approach is based on the concept of a “Header Point” which maintains a concise summary of visited region in the data space. This summary allows some fraction of non-skyline tuples to be pruned from the set advancing to the skyline processing phase, thus reducing the number of expensive dominance checks required in the skyline phase. We further present more aggressive pruning rules that result in the computation of near-complete skylines in significantly less time than the complete algorithm. A comprehensive performance evaluation of different algorithms is presented using datasets with different types of data distributions generated using a benchmark data generator.".
- 49 hasAuthorList authorList.
- 49 isPartOf proceedings.
- 49 keyword "RDF Databases".
- 49 keyword "Skyline Queries".
- 49 keyword "Skyline-Join".
- 49 title "Efficiently Evaluating Skyline Queries on RDF Databases".
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- item-1 hasContent ling-chen.
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- item-2 hasContent sidan-gao.
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- item-3 hasContent kemafor-anyanwu.
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- 52 creator gianluca-correndo.
- 52 creator manuel-salvadores.
- 52 creator nicholas-gibbins.
- 52 creator nigel-r-shadbolt.
- 52 creator steve-harris.
- 52 type InProceedings.
- 52 label "The Design and Implementation of Minimal RDFS Backward Reasoning in 4store".
- 52 sameAs 52.
- 52 abstract "This paper describes the design and implementation of Minimal RDFS semantics based on a backward chaining approach and implemented on a clustered RDF triplestore. The system presented, called 4sr, uses 4store as base infrastructure. In order to achieve a highly scalable system we implemented the reasoning at the lowest level of the quad store, the bind operation. The bind operation runs concurrently in all the data slices allowing the reasoning to be processed in parallel among the cluster. Throughout this paper we provide detailed descriptions of the architecture, reasoning algorithms, and a scalability evaluation with the LUBM benchmark. 4sr is a stable tool available under a GNU GPL3 license and can be freely used and extended by the community.".
- 52 hasAuthorList authorList.
- 52 isPartOf proceedings.
- 52 keyword "RDFS".
- 52 keyword "Reasoning".
- 52 keyword "SPARQL".
- 52 keyword "Scalability".
- 52 keyword "Triple Store".
- 52 title "The Design and Implementation of Minimal RDFS Backward Reasoning in 4store".
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- item-1 type ListItem.
- item-1 hasContent manuel-salvadores.
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- item-2 hasContent gianluca-correndo.
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- item-3 hasContent steve-harris.
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- item-4 hasContent nicholas-gibbins.
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- item-5 hasContent nigel-r-shadbolt.
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- 7 creator daniel-blum.
- 7 creator sara-cohen.
- 7 type InProceedings.
- 7 label "Grr: Generating Random RDF".
- 7 sameAs 7.
- 7 abstract "This paper presents Grr, a powerful system for generating random RDF data, which can be used to test Semantic Web applications. Grr has a SPARQL-like syntax, which allows the system to be both powerful and convenient. It is shown that Grr can easily be used to produce intricate datasets, such as the LUBM benchmark. Optimization techniques are employed, which make the generation process efficient and scalable.".
- 7 hasAuthorList authorList.
- 7 isPartOf proceedings.
- 7 keyword "RDF".
- 7 keyword "Semantic Web".
- 7 keyword "data generation".
- 7 title "Grr: Generating Random RDF".
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- item-1 hasContent daniel-blum.
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- item-2 hasContent sara-cohen.
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- 8 creator bob-adolf.
- 8 creator david-haglin.
- 8 creator david-mizell.
- 8 creator edward-jimenez.
- 8 creator eric-goodman.
- 8 creator sinan-al-saffar.
- 8 type InProceedings.
- 8 label "High-performance Computing Applied to Semantic Web Databases".