Efficiently Joining Group Patterns in SPARQL Queries

Vidal, María-Esther; Ruckhaus, Edna; Lampo, Tomas; Martínez, Amadís; Sierra, Javier E.; Polleres, Axel

doi:10.1007/978-3-642-13486-9_16

Cited by 67 publications

(40 citation statements)

References 16 publications

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“…We have also experimented with different datasets using the SP 2 B benchmark [19] as well as a real world dataset of the US Congress vote results presented in [26]. The results were similar to the ones observed using LUBM.…”

Section: Discussionsupporting

confidence: 62%

SPARQL Query Optimization on Top of DHTs

Kaoudi

Kyzirakos

Koubarakis

2010

Lecture Notes in Computer Science

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Abstract. We study the problem of SPARQL query optimization on top of distributed hash tables. Existing works on SPARQL query processing in such environments have never been implemented in a real system, or do not utilize any optimization techniques and thus exhibit poor performance. Our goal in this paper is to propose efficient and scalable algorithms for optimizing SPARQL basic graph pattern queries. We augment a known distributed query processing algorithm with query optimization strategies that improve performance in terms of query response time and bandwidth usage. We implement our techniques in the system Atlas and study their performance experimentally in a local cluster.

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Section: Discussionsupporting

confidence: 62%

SPARQL Query Optimization on Top of DHTs

Kaoudi

Kyzirakos

Koubarakis

2010

Lecture Notes in Computer Science

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“…We also have preliminary positive results on a simple cost-base optimization techniques that uses rank-join algorithms [13,11] in combination with star-shaped patterns identification [23]. In addition, we plan to perform an exhaustive comparison with the 2.8.9 version of the Jena ARQ query engine, which recently included an ad-hoc optimization for top-k queries, where the OR-DER BY and LIMIT clauses are still evaluated after the completion of the other operations, but they are merged into a single operator with a priority queue that contains k ordered mappings.…”

Section: Resultsmentioning

confidence: 99%

Extending SPARQL Algebra to Support Efficient Evaluation of Top-K SPARQL Queries

2012

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Abstract. With the widespread adoption of Linked Data, the efficient processing of SPARQL queries gains importance. A crucial category of queries that is prone to optimization is "top-k" queries, i.e. queries returning the top k results ordered by a specified ranking function. Top-k queries can be expressed in SPARQL by appending to a SELECT query the ORDER BY and LIMIT clauses, which impose a sorting order on the result set, and limit the number of results. However, the ORDER BY and LIMIT clauses in SPARQL algebra are result modifiers, i.e. their evaluation is performed only after the evaluation of the other query clauses. The evaluation of ORDER BY and LIMIT clauses in SPARQL engines typically requires the process of all the matching solutions (possibly thousands), followed by a monolithically computation of the ranking function for each solution, even if only a limited number (e.g. K = 10) of them were requested, thus leading to poor performance. In this paper, we present SPARQL-RANK, an extension of the SPARQL algebra and execution model that supports ranking as a first-class SPAR-QL construct. The new algebra and execution model allow for splitting the ranking function and interleaving it with other operations. We also provide a prototypal open source implementation of SPARQL-RANK based on ARQ, and we carry out a series of preliminary experiments.

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“…For the other triple patterns, they are grouped into starshaped groups, i.e., sets of triple patterns with just one variable in common are evaluated in the same sub-queries. The use of star-shaped groups may reduce the size of intermediate results, and the number of tuples transferred from endpoints to the query engine, as suggested by Vidal et al [35].…”

Section: Query Decomposition In Federations Of Sparqlmentioning

confidence: 99%

Decomposing federated queries in presence of replicated fragments

Montoya

Skaf-Molli

Molli

et al. 2017

Journal of Web Semantics

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Federated query engines allow for linked data consumption using SPARQL endpoints. Replicating data fragments from different sources enables data re-organization and provides the basis for more effective and efficient federated query processing. However, existing federated query engines are not designed to support replication. In this paper, we propose a replication-aware framework named LILAC, sparqL query decomposItion against federations of repLicAted data sourCes, that relies on replicated fragment descriptions to accurately identify sources that provide replicated data. We defined the query decomposition problem with fragment replication (QDP-FR). QDP-FR corresponds to the problem of finding the sub-queries to be sent to the endpoints that allows the federated query engine to compute the query answer, while the number of tuples to be transferred from endpoints to the federated query engine is minimized. An approximation of QDP-FR is implemented by the LILAC replication-aware query decomposition algorithm. Further, LILAC techniques have been included in the state-of-the-art federated query engines FedX and ANAPSID to evaluate the benefits of the proposed source selection and query decomposition techniques in different engines. Experimental results suggest that LILAC efficiently solves QDP-FR and is able to reduce the number of transferred tuples and the execution time of the studied engines.

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Efficiently Joining Group Patterns in SPARQL Queries

Cited by 67 publications

References 16 publications

SPARQL Query Optimization on Top of DHTs

SPARQL Query Optimization on Top of DHTs

Extending SPARQL Algebra to Support Efficient Evaluation of Top-K SPARQL Queries

Decomposing federated queries in presence of replicated fragments

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