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.-C. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints2020, 2020050360. https://doi.org/10.20944/preprints202005.0360.v3
Ali, W.; Saleem, M.; Yao, B.; Hogan, A.; Ngonga Ngomo, A.-C. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints 2020, 2020050360. https://doi.org/10.20944/preprints202005.0360.v3
Ali, W.; Saleem, M.; Yao, B.; Hogan, A.; Ngonga Ngomo, A.-C. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints2020, 2020050360. https://doi.org/10.20944/preprints202005.0360.v3
APA Style
Ali, W., Saleem, M., Yao, B., Hogan, A., & Ngonga Ngomo, A. C. (2020). Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. Preprints. https://doi.org/10.20944/preprints202005.0360.v3
Chicago/Turabian Style
Ali, W., Aidan Hogan and Axel-Cyrille Ngonga Ngomo. 2020 "Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey" Preprints. https://doi.org/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.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Waqas Ali
Commenter's Conflict of Interests: Author