Foundations of SPARQL query optimization

Schmidt, Michaël; Meier, Michael; Lausen, Georg

doi:10.1145/1804669.1804675

Cited by 237 publications

(247 citation statements)

References 34 publications

(151 reference statements)

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“…In [11,17], the authors study the complexity of evaluating the fragment of SPARQL consisting of the operators AND, UNION, OPT and FILTER. One of the conclusions of these papers is that the main source of complexity in SPARQL comes from the use of the OPT operator.…”

Section: Optimization Via Well-designed Patternsmentioning

confidence: 99%

“…Besides, we implement the optimizations proposed in [11], using the notion of welldesigned patterns, which prove to be effective in the optimization of queries that contain the OPTIONAL operator, the most costly operator in SPARQL [11,17]. This has also important implications in the number of tuples being transferred and joined in federated queries, and hence our implementation benefits from this.…”

Section: Introductionmentioning

confidence: 99%

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Semantics and Optimization of the SPARQL 1.1 Federation Extension

Buil-Aranda

Arenas²,

Corcho

2011

Lecture Notes in Computer Science

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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.

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Section: Optimization Via Well-designed Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantics and Optimization of the SPARQL 1.1 Federation Extension

Buil-Aranda

Arenas²,

Corcho

2011

Lecture Notes in Computer Science

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“…We choose SPARQL as a query language: It is known to be relationally complete, allowing us to encode a broad range of queries with varying complexity, from simple conjunctive queries to complex requests involving e.g. negation [4,20,22]. We restrict ourselves on general characteristics, pointing to the FedBench project page for a complete listing and description.…”

Section: Benchmark Queriesmentioning

confidence: 99%

“…(Q1) Query Language: The expressiveness of the query language needed by applications may vary from case to case: while some applications get around with simple conjunctive queries, others may rely on the full expressive power of RDF query languages, such as the de facto standard SPARQL [20,22]. (Q2) Result Completeness: Certain applications may rely on complete results, while others cannot afford it when responsiveness is first priority.…”

Section: Introductionmentioning

confidence: 99%

FedBench: A Benchmark Suite for Federated Semantic Data Query Processing

Schmidt

Görlitz

Haase

et al. 2011

Lecture Notes in Computer Science

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117

108

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Abstract. In this paper we present FedBench, a comprehensive benchmark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face different settings at both the data and query level, such as varying data access interfaces, incomplete knowledge about data sources, availability of different statistics, and varying degrees of query expressiveness. Accounting for this heterogeneity, we present a highly flexible benchmark suite, which can be customized to accommodate a variety of use cases and compare competing approaches. We discuss design decisions, highlight the flexibility in customization, and elaborate on the choice of data and query sets. The practicability of our benchmark is demonstrated by a rigorous evaluation of various application scenarios, where we indicate both the benefits as well as limitations of the state-of-the-art federated query processing strategies for semantic data.

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“…Existing approaches focus on algebraic [11,13] or selectivity-based optimizations [14]. Despite an increasing need from practitioners [5,6], few works address top-k query optimization in SPARQL.…”

Section: Related Workmentioning

confidence: 99%

Efficient Execution of Top-K SPARQL Queries

Magliacane

Bozzon

Valle

2012

The Semantic Web – ISWC 2012

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Abstract. Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The SPARQL-RANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for SPARQL-RANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a SPARQL-RANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.

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Foundations of SPARQL query optimization

Cited by 237 publications

References 34 publications

Semantics and Optimization of the SPARQL 1.1 Federation Extension

Semantics and Optimization of the SPARQL 1.1 Federation Extension

FedBench: A Benchmark Suite for Federated Semantic Data Query Processing

Efficient Execution of Top-K SPARQL Queries

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