Preprint Review Version 3 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 21 May 2020 / Approved: 23 May 2020 / Online: 23 May 2020 (05:32:15 CEST)
Version 2 : Received: 30 May 2020 / Approved: 31 May 2020 / Online: 31 May 2020 (18:00:42 CEST)
Version 3 : Received: 26 August 2020 / Approved: 28 August 2020 / Online: 28 August 2020 (09:38:00 CEST)

How to cite: Ali, W.; Saleem, M.; Yao, B.; Hogan, A.; Ngonga Ngomo, A. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints 2020, 2020050360 (doi: 10.20944/preprints202005.0360.v3). Ali, W.; Saleem, M.; Yao, B.; Hogan, A.; Ngonga Ngomo, A. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints 2020, 2020050360 (doi: 10.20944/preprints202005.0360.v3).

Abstract

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.

Subject Areas

Storage; Indexing; Language; Query Planning; SPARQL Translation; Centralized RDF Engines; Distributed RDF Engines; SPARQL Benchmarks; Survey

Comments (1)

Comment 1
Received: 28 August 2020
Commenter: Waqas Ali
Commenter's Conflict of Interests: Author
Comment: Details of the systems have been updated
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