A Comprehensive Study for Essentiality of Graph Based Distributed SPARQL Query Processing

Yasin, Muhammad Qasim; Zhang, Xiaowang; Haq, Rafiul; Feng, Zhiyong; Yitagesu, Sofonias

doi:10.1007/978-3-319-91455-8_15

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…[218] 2020 Short Survey ∼ ∼ 24 Alaoui [8] 2019 Short Survey ∼ 30+ Janke et al [91] 2018 Chapter ∼ ∼ ∼ ∼ 50+ 9 Wylot et. al [211] 2018 Survey ∼ ∼ ∼ 24 8 Pan et al [146] 2018 Short survey ∼ ∼ ∼ 25+ 4 Yasin et al [216] 2018 Short survey ∼ ∼ 14 Elzein et al [52] 2018 Survey ∼ ∼ ∼ 15+ Abdelaziz et al [3] 2017 Comparison ∼ ∼ ∼ 21 4 Ma et al [122] 2016 Survey ∼ ∼ 17 6 Özsu [145] 2016 Survey ∼ ∼ ∼ 35+ Kaoudi et al [96] 2015 Survey ∼ ∼ ∼ 17 Faye et al [55] 2012 Survey ∼ ∼ 13 Luo et al [119] 2012 Chapter 20+ Svoboda et al [187] 2011…”

Section: Literature Reviewunclassified

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Saleem²,

Yao³

et al. 2021

Preprint

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Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

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Section: Literature Reviewunclassified

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Saleem²,

Yao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Yasin et al [129] discussed the limitations and discrepancies in distributed RDF engines. In particular, they discussed the SPARQL 1.1 support in these engines.…”

Section: Literature Reviewmentioning

confidence: 99%

“…al [93] 2018 Storage and query evaluation in centralized and distributed RDF engines Wylot et. al [127] 2018 Storage and indexing in centralized and distributed RDF engines Pan et al [81] 2018 Storage techniques in centralized and distributed RDF engines Yasin et al [129] 2018 Suitability and query evaluation of distributed RDF engines Elzein et al [28] 2018 Storage and query evaluation in RDF engines on cloud Abdelaziz et al [3] 2017 Performance evaluation of distributed RDF engines Ma et al [71] 2016 Storage techniques in centralized RDF engines Ozsu [80] 2016 General overview of centralized and distributed RDF engines Kaoudi et al [58] 2015 RDF engines on cloud Faye et al [31] 2012 Storage and Indexing techniques in centralized RDF engines Svoboda et al [114] 2011 Indexing in centralized, distributed, and global RDF engines Sakr et al [92] 2010 Storage and query evaluation in SPARQL transnational RDF engines Data storage is an integral component of any Database Management System (DBMS). Efficient data storage is critical for disk space consumption, security, scalability, maintenance, and performance of the DBMS.…”

Section: Studymentioning

confidence: 99%

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

Ali¹

2020

Preprint

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“…Yasin et al [128] discussed the limitations and discrepancies in distributed RDF engines. In particular, they discussed the SPARQL 1.1 support in these engines.…”

Section: Literature Reviewmentioning

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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A Comprehensive Study for Essentiality of Graph Based Distributed SPARQL Query Processing

Cited by 7 publications

References 32 publications

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

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

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

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