FedX: Optimization Techniques for Federated Query Processing on Linked Data

Schwarte, Andreas; Haase, Peter; Hose, Katja; Schenkel, Ralf; Schmidt, Michaël

doi:10.1007/978-3-642-25073-6_38

Cited by 243 publications

(262 citation statements)

References 20 publications

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“…In addition, many have implemented federation that doesn't require explicit references to service endpoints, e.g. [8]. A recent scientific treatment of the current specification is in [1].…”

Section: In Sparql Federationmentioning

confidence: 99%

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Sharing Statistics for SPARQL Federation Optimization, with Emphasis on Benchmark Quality

Kjernsmo

2012

Lecture Notes in Computer Science

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Abstract. Federation of semantic data on SPARQL endpoints will allow data to remain distributed so that it can be controlled by local curators and swiftly updated. There are considerable performance problems, which the present work proposes to address, mainly by computation and exposure of statistical digests to assist selectivity estimation.For an objective evaluation as well as comparison of engines, benchmarks that exhaustively covers the parameter space is required. We propose an investigation into this problem using statistical experimental planning. MotivationQuery federation with SPARQL, which is a standardized query language for the Semantic Web, has attracted much attention from industry and academia alike, and four implementations of basic query federation were submitted to the SPARQL 1.1 Working Group as input for the forthcoming work 1 . This feature was supported by many group members, and the Last Call working draft of the proposed standard was published on 17 November 2011.While the basic feature set of the proposed standard can enable users to create federated queries, it is not of great use as it requires extensive prior knowledge of both the data to be queried and performance characteristics of the involved query engines. Without this knowledge, the overall performance is insufficient for most practical applications.To aid optimization, SPARQL endpoints should expose details about both data and performance characteristics of the engine itself. The proposed work has two focal points: Statistical digests of data for optimizations and benchmarking SPARQL engines.The focus on SPARQL benchmarking is not only motivated from the perspective of optimization, as I have found the current state of the art in SPARQL benchmarking lacking in its use of statistics. The emphasis in the present paper is on statistics in benchmarking with the purpose of providing a firmer foundation on which assertions about engine performance can be backed with evidence.

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Section: In Sparql Federationmentioning

confidence: 99%

“…A recent review of the state of the art is in [4]. In addition, [8] proposes bound joins and proves they can dramatically reduce the number of requests to federation members that are needed, as well as the implementation of FedX.…”

Section: In Sparql Federationmentioning

confidence: 99%

Sharing Statistics for SPARQL Federation Optimization, with Emphasis on Benchmark Quality

Kjernsmo

2012

Lecture Notes in Computer Science

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“…As a result, certain queries can only be answered by retrieving information from several data sources. This type of queries, called federated queries, are becoming increasingly popular within the Web of Data [1,3,8,9,12,14,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…We extend three well-known federated query engines -DARQ [21], SPLEN-DID [8], and FedX [22] -with DAW, and compare these extensions with the original frameworks. The comparison shows that DAW requires fewer sources for each of the query's triple pattern, therefore improving query execution times.…”

Section: Introductionmentioning

confidence: 99%

DAW: Duplicate-AWare Federated Query Processing over the Web of Data

Saleem

Ngomo

Parreira³

et al. 2013

Advanced Information Systems Engineering

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Abstract. Over the last years the Web of Data has developed into a large compendium of interlinked data sets from multiple domains. Due to the decentralised architecture of this compendium, several of these datasets contain duplicated data. Yet, so far, only little attention has been paid to the effect of duplicated data on federated querying. This work presents DAW, a novel duplicate-aware approach to federated querying over the Web of Data. DAW is based on a combination of min-wise independent permutations and compact data summaries. It can be directly combined with existing federated query engines in order to achieve the same query recall values while querying fewer data sources. We extend three well-known federated query processing engines -DARQ, SPLENDID, and FedX -with DAW and compare our extensions with the original approaches. The comparison shows that DAW can greatly reduce the number of queries sent to the endpoints, while keeping high query recall values. Therefore, it can significantly improve the performance of federated query processing engines. Moreover, DAW provides a source selection mechanism that maximises the query recall, when the query processing is limited to a subset of the sources.

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“…This fact, together with the potential added value that can be obtained from the combination of such distributed data sources, has motivated the development of systems that allow executing queries over federated SPARQL endpoints (e.g., SPARQL-DQP [2], Jena's ARQ 1 , RDF::Query 2 , ANAPSID [1], FedX [10], ADERIS [3]). Some systems use SPARQL 1.0 or adhoc extensions, while others rely on the query federation extensions that are being proposed as part of the upcoming SPARQL 1.1 specification [6].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?

Montoya

Vidal

Corcho

et al. 2012

The Semantic Web – ISWC 2012

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Abstract. Testbeds proposed so far to evaluate, compare, and eventually improve SPARQL query federation systems have still some limitations. Some variables and configurations that may have an impact on the behavior of these systems (e.g., network latency, data partitioning and query properties) are not sufficiently defined; this affects the results and repeatability of independent evaluation studies, and hence the insights that can be obtained from them. In this paper we evaluate FedBench, the most comprehensive testbed up to now, and empirically probe the need of considering additional dimensions and variables. The evaluation has been conducted on three SPARQL query federation systems, and the analysis of these results has allowed to uncover properties of these systems that would normally be hidden with the original testbeds.

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FedX: Optimization Techniques for Federated Query Processing on Linked Data

Cited by 243 publications

References 20 publications

Sharing Statistics for SPARQL Federation Optimization, with Emphasis on Benchmark Quality

Sharing Statistics for SPARQL Federation Optimization, with Emphasis on Benchmark Quality

DAW: Duplicate-AWare Federated Query Processing over the Web of Data

Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?

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