CostFed: Cost-Based Query Optimization for SPARQL Endpoint Federation

Saleem, Muhammad; Potocki, Alexander; Soru, Tommaso; Hartig, Olaf; Ngomo, Axel-Cyrille Ngonga

doi:10.1016/j.procs.2018.09.016

Cited by 39 publications

(49 citation statements)

References 3 publications

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“…We represent any basic graph pattern (BGP) of a given SPARQL query as a directed hypergraph (DH) [25], a generalization of a directed graph in which a hyperedge can join any number of vertices. In our specific case, every hyperedge captures a triple pattern.…”

Section: Sparql Queriesmentioning

confidence: 99%

How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

Saleem

Szárnyas

Conrads

et al. 2019

The World Wide Web Conference

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Triplestores are data management systems for storing and querying RDF data. Over recent years, various benchmarks have been proposed to assess the performance of triplestores across different performance measures. However, choosing the most suitable benchmark for evaluating triplestores in practical settings is not a trivial task. This is because triplestores experience varying workloads when deployed in real applications. We address the problem of determining an appropriate benchmark for a given real-life workload by providing a fine-grained comparative analysis of existing triplestore benchmarks. In particular, we analyze the data and queries provided with the existing triplestore benchmarks in addition to several real-world datasets. Furthermore, we measure the correlation between the query execution time and various SPARQL query features and rank those features based on their significance levels. Our experiments reveal several interesting insights about the design of such benchmarks. With this fine-grained evaluation, we aim to support the design and implementation of more diverse benchmarks. Application developers can use our result to analyze their data and queries and choose a data management system.

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Section: Sparql Queriesmentioning

confidence: 99%

How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

Saleem

Szárnyas

Conrads

et al. 2019

The World Wide Web Conference

Self Cite

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“…For complete details, readers are encouraged to look at original W3C sources of RDF 6 and SPARQL 7 . This discussion is adapted from [80,98,50,94].…”

Section: Basic Concepts and Definitionsmentioning

confidence: 99%

“…Any BGP of a given SPARQL query can be represented as directed hypergraph (DH) [98], a general form of a directed graph in which a hyperedge can be used to join any number of vertices. This representation shows that every hyperedge can capture a triple pattern.…”

Section: Sparqlmentioning

confidence: 99%

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Ali¹,

Saleem²,

Yao³

et al. 2020

Preprint

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The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

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“…In this sub-section, bind join ∀ and symmetric hash join ∀ ℎ are applied for query planning between the two cost estimations [17], which is mathematically represented in the Eq. (1), (2), and (3).…”

Section: Cost Value Computationmentioning

confidence: 99%

“…Step 4: Determine the best hunt agent , second and third best hunt agents and by using the Eqs. (12)- (17).…”

Section: Overview Of Modified Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Semantic Web Query Join Optimization Using Modified Grey Wolf Optimization Algorithm

Jose¹,

Poulose²

2019

IJIES

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Presently, the distributed Resource Description Framework (RDF) partition the data across several computer nodes. In that, many existing RDF systems results in expensive query evaluation and high start-up cost. To address these issues, a new optimization algorithm: modified Grey Wolf Optimization (GWO) has been developed in this research paper. In conventional GWO algorithm, after finding the best values of , and , stopping criteria is accomplished. In modified GWO algorithm, after finding the best values of , and , the alpha value once again encircles the possible solutions for obtaining an optimal solution. In RDF data, query optimization is a challenging task, which has been effectively handled by modified GWO algorithm. In the experimental phase, modified GWO showed good performance in terms of execution time and memory usage as compared to the existing methodologies: Partial Evaluation and Centralized Assembly (PECA), Partial Evaluation and Distributed Assembly (PEDA), RDF-3X, Graph-Based SPARQL Query Engine (gStore), and Legato on Lehigh University Benchmark (LUBM) 10000 and DOREMUS 2017 datasets. Compared to these existing systems, the proposed system reduced the execution time around 2-5 minutes, and improves the precision, recall, and f-measure around 2-7%.

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CostFed: Cost-Based Query Optimization for SPARQL Endpoint Federation

Cited by 39 publications

References 3 publications

How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Semantic Web Query Join Optimization Using Modified Grey Wolf Optimization Algorithm

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