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Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and int...

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Detalles Bibliográficos
Autores principales: Lowe, Joy, Matthee, Machdel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134219/
http://dx.doi.org/10.1007/978-3-030-44999-5_39
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author Lowe, Joy
Matthee, Machdel
author_facet Lowe, Joy
Matthee, Machdel
author_sort Lowe, Joy
collection PubMed
description The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.
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spelling pubmed-71342192020-04-06 Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review Lowe, Joy Matthee, Machdel Responsible Design, Implementation and Use of Information and Communication Technology Article The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration. 2020-03-06 /pmc/articles/PMC7134219/ http://dx.doi.org/10.1007/978-3-030-44999-5_39 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Lowe, Joy
Matthee, Machdel
Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title_full Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title_fullStr Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title_full_unstemmed Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title_short Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review
title_sort requirements of data visualisation tools to analyse big data: a structured literature review
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134219/
http://dx.doi.org/10.1007/978-3-030-44999-5_39
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