Semantic Information Retrieval Systems Costing in Big Data Environment
Nowadays, dealing with big data is a major challenge for application developers and researchers in several domains like storage, processing, indexing, integration, governance and semantic search. For decision-making and analysis purpose, semantic retrieval of information from big data is gaining mor...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer, Cham
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27139/1/Semantic%20Information%20Retrieval%20Systems%20Costing%20in%20Big%20Data%20Environment1.pdf |
| _version_ | 1825813111756554240 |
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| author | Mahmood, Khalid Rahmah, Mokhtar Ahmed, Md. Manjur Raza, Muhammad Ahsan |
| author_facet | Mahmood, Khalid Rahmah, Mokhtar Ahmed, Md. Manjur Raza, Muhammad Ahsan |
| author_sort | Mahmood, Khalid |
| collection | UMP |
| description | Nowadays, dealing with big data is a major challenge for application developers and researchers in several domains like storage, processing, indexing, integration, governance and semantic search. For decision-making and analysis purpose, semantic retrieval of information from big data is gaining more attention with the need of extracting accurate, meaningful and relevant results. Several semantic information retrieval techniques alternatively have been developed by researchers for retrieval of valuable information in big data environment. This article classifies literature and presents an analysis of five recent semantic information retrieval systems in terms of their methodologies, strengths and limitations. In addition, we evaluate these schemes on the basis of specific datasets and performance measures such as precision, recall and f-measure metrics. A comparative analysis of performance measures shows that IBRI-CASONTO achieves best f-measure value of 97.6 over other information retrieval systems. |
| first_indexed | 2024-03-06T12:39:11Z |
| format | Conference or Workshop Item |
| id | UMPir27139 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:39:11Z |
| publishDate | 2020 |
| publisher | Springer, Cham |
| record_format | dspace |
| spelling | UMPir271392020-03-25T00:15:43Z http://umpir.ump.edu.my/id/eprint/27139/ Semantic Information Retrieval Systems Costing in Big Data Environment Mahmood, Khalid Rahmah, Mokhtar Ahmed, Md. Manjur Raza, Muhammad Ahsan QA75 Electronic computers. Computer science Nowadays, dealing with big data is a major challenge for application developers and researchers in several domains like storage, processing, indexing, integration, governance and semantic search. For decision-making and analysis purpose, semantic retrieval of information from big data is gaining more attention with the need of extracting accurate, meaningful and relevant results. Several semantic information retrieval techniques alternatively have been developed by researchers for retrieval of valuable information in big data environment. This article classifies literature and presents an analysis of five recent semantic information retrieval systems in terms of their methodologies, strengths and limitations. In addition, we evaluate these schemes on the basis of specific datasets and performance measures such as precision, recall and f-measure metrics. A comparative analysis of performance measures shows that IBRI-CASONTO achieves best f-measure value of 97.6 over other information retrieval systems. Springer, Cham 2020-12-05 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27139/1/Semantic%20Information%20Retrieval%20Systems%20Costing%20in%20Big%20Data%20Environment1.pdf Mahmood, Khalid and Rahmah, Mokhtar and Ahmed, Md. Manjur and Raza, Muhammad Ahsan (2020) Semantic Information Retrieval Systems Costing in Big Data Environment. In: Recent Advances on Soft Computing and Data Mining. Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020) , 22-23 January 2020 , Melaka, Malaysia. pp. 192-201., 978. ISBN 978-3-030-36055-9 (Published) https://doi.org/10.1007/978-3-030-36056-6_19 |
| spellingShingle | QA75 Electronic computers. Computer science Mahmood, Khalid Rahmah, Mokhtar Ahmed, Md. Manjur Raza, Muhammad Ahsan Semantic Information Retrieval Systems Costing in Big Data Environment |
| title | Semantic Information Retrieval Systems Costing in Big Data Environment |
| title_full | Semantic Information Retrieval Systems Costing in Big Data Environment |
| title_fullStr | Semantic Information Retrieval Systems Costing in Big Data Environment |
| title_full_unstemmed | Semantic Information Retrieval Systems Costing in Big Data Environment |
| title_short | Semantic Information Retrieval Systems Costing in Big Data Environment |
| title_sort | semantic information retrieval systems costing in big data environment |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/27139/1/Semantic%20Information%20Retrieval%20Systems%20Costing%20in%20Big%20Data%20Environment1.pdf |
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