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Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)

The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use...

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Main Authors: Ghani, Imran, Seung, Ryul Jeong
Format: Article
Published: Korean Society for Internet Information 2014
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/62553/
http://dx.doi.org/10.3837/tiis.2014.06.012
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spelling my.utm.625532017-06-18T06:15:38Z http://eprints.utm.my/id/eprint/62553/ Semantic computing for big data: approaches, tools, and emerging directions (2011-2014) Ghani, Imran Seung, Ryul Jeong QA75 Electronic computers. Computer science The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data. Korean Society for Internet Information 2014 Article PeerReviewed Ghani, Imran and Seung, Ryul Jeong (2014) Semantic computing for big data: approaches, tools, and emerging directions (2011-2014). KSII Transactions on Internet and Information Systems, 8 (6). pp. 2022-2042. ISSN 1976-7277 http://dx.doi.org/10.3837/tiis.2014.06.012 DOI:10.3837/tiis.2014.06.012
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ghani, Imran
Seung, Ryul Jeong
Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
description The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.
format Article
author Ghani, Imran
Seung, Ryul Jeong
author_facet Ghani, Imran
Seung, Ryul Jeong
author_sort Ghani, Imran
title Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
title_short Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
title_full Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
title_fullStr Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
title_full_unstemmed Semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
title_sort semantic computing for big data: approaches, tools, and emerging directions (2011-2014)
publisher Korean Society for Internet Information
publishDate 2014
url http://eprints.utm.my/id/eprint/62553/
http://dx.doi.org/10.3837/tiis.2014.06.012
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score 13.211869

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