Distributed Join Query Processing for Big RDF Data
The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meet...
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| Format: | Article |
| Language: | English |
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American Scientific Publisher
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf |
| _version_ | 1825811900940681216 |
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| author | Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio |
| author_facet | Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio |
| author_sort | Elzein, Nahla Mohammed |
| collection | UMP |
| description | The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. Moreover, efficient data storage and retrieval that can scale to large amounts of possibly schema-less data have proven to be quite difficult to achieve, specifically, RDF data storage with complex and large graph patterns for representing semantic data, and SPARQL query languages. In this paper, we provide comprehensive discussion about the proposed algorithms of Join.Query processing of RDF data by considering MapReduce Framework in a distributed environment. Moreover, we introduced a framework for RDF query processing and the benchmark that is used for the performance evaluation. Finally, we offer an evaluation discussion on distributed join query processing for big RDF data. |
| first_indexed | 2024-03-06T12:21:14Z |
| format | Article |
| id | UMPir20172 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:21:14Z |
| publishDate | 2018 |
| publisher | American Scientific Publisher |
| record_format | dspace |
| spelling | UMPir201722018-11-27T01:55:34Z http://umpir.ump.edu.my/id/eprint/20172/ Distributed Join Query Processing for Big RDF Data Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio QA76 Computer software The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. Moreover, efficient data storage and retrieval that can scale to large amounts of possibly schema-less data have proven to be quite difficult to achieve, specifically, RDF data storage with complex and large graph patterns for representing semantic data, and SPARQL query languages. In this paper, we provide comprehensive discussion about the proposed algorithms of Join.Query processing of RDF data by considering MapReduce Framework in a distributed environment. Moreover, we introduced a framework for RDF query processing and the benchmark that is used for the performance evaluation. Finally, we offer an evaluation discussion on distributed join query processing for big RDF data. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf Elzein, Nahla Mohammed and Mazlina, Abdul Majid and Fakherldin, Mohammed and Hashem, Ibrahim Abaker Targio (2018) Distributed Join Query Processing for Big RDF Data. Advanced Science Letters, 24 (10). p. 96. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2018.13013 doi: 10.1166/asl.2018.13013 |
| spellingShingle | QA76 Computer software Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio Distributed Join Query Processing for Big RDF Data |
| title | Distributed Join Query Processing for Big RDF Data |
| title_full | Distributed Join Query Processing for Big RDF Data |
| title_fullStr | Distributed Join Query Processing for Big RDF Data |
| title_full_unstemmed | Distributed Join Query Processing for Big RDF Data |
| title_short | Distributed Join Query Processing for Big RDF Data |
| title_sort | distributed join query processing for big rdf data |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf |
| work_keys_str_mv | AT elzeinnahlamohammed distributedjoinqueryprocessingforbigrdfdata AT mazlinaabdulmajid distributedjoinqueryprocessingforbigrdfdata AT fakherldinmohammed distributedjoinqueryprocessingforbigrdfdata AT hashemibrahimabakertargio distributedjoinqueryprocessingforbigrdfdata |