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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...

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Main Authors: Mahmood, Khalid, Rahmah, Mokhtar, Ahmed, Md. Manjur, Raza, Muhammad Ahsan
Format: Conference or Workshop Item
Language:English
Published: Springer, Cham 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/27139/1/Semantic%20Information%20Retrieval%20Systems%20Costing%20in%20Big%20Data%20Environment1.pdf
http://umpir.ump.edu.my/id/eprint/27139/
https://doi.org/10.1007/978-3-030-36056-6_19
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spelling my.ump.umpir.271392020-03-25T00:15:43Z 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 https://doi.org/10.1007/978-3-030-36056-6_19
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
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
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.
format Conference or Workshop Item
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
title Semantic Information Retrieval Systems Costing in Big Data Environment
title_short 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_sort semantic information retrieval systems costing in big data environment
publisher Springer, Cham
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/27139/1/Semantic%20Information%20Retrieval%20Systems%20Costing%20in%20Big%20Data%20Environment1.pdf
http://umpir.ump.edu.my/id/eprint/27139/
https://doi.org/10.1007/978-3-030-36056-6_19
_version_ 1662754759501676544
score 13.211869

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