BigData visualization: Parallel coordinates using density approach

Zhang, J; Meng, Z; Huang, ML

BigData visualization: Parallel coordinates using density approach

Zhang, J

Meng, Z Huang, ML

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Publication Type:: Conference Proceeding
Citation:: 2014 2nd International Conference on Systems and Informatics, ICSAI 2014, 2015, pp. 1056 - 1063
Issue Date:: 2015-01-13

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.contributor.author	Meng, Z	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.date.issued	2015-01-13	en_US
dc.identifier.citation	2014 2nd International Conference on Systems and Informatics, ICSAI 2014, 2015, pp. 1056 - 1063	en_US
dc.identifier.isbn	9781479954582	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34604
dc.description.abstract	© 2014 IEEE. Information visualization is a very important tool in BigData analytics. BigData, structured and unstructured data which contains images, videos, texts, audio and other forms of data, collected from multiple datasets, is too big, too complex and moves too fast to analyse using traditional methods. This has given rise to two issues; 1) how to reduce multidimensional data without the loss of any data patterns for multiple datasets, 2) how to visualize BigData patterns for analysis. In this paper, we have classified the BigData attributes into '5Ws' data dimensions, and then established a '5Ws' density approach that represents the characteristics of data flow patterns. We use parallel coordinates to display the '5Ws' sending and receiving densities which provide more analytic features for BigData analysis. The experiment shows that this new model with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.	en_US
dc.relation.ispartof	2014 2nd International Conference on Systems and Informatics, ICSAI 2014	en_US
dc.relation.isbasedon	10.1109/ICSAI.2014.7009441	en_US
dc.title	BigData visualization: Parallel coordinates using density approach	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Information visualization is a very important tool in BigData analytics. BigData, structured and unstructured data which contains images, videos, texts, audio and other forms of data, collected from multiple datasets, is too big, too complex and moves too fast to analyse using traditional methods. This has given rise to two issues; 1) how to reduce multidimensional data without the loss of any data patterns for multiple datasets, 2) how to visualize BigData patterns for analysis. In this paper, we have classified the BigData attributes into '5Ws' data dimensions, and then established a '5Ws' density approach that represents the characteristics of data flow patterns. We use parallel coordinates to display the '5Ws' sending and receiving densities which provide more analytic features for BigData analysis. The experiment shows that this new model with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

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http://hdl.handle.net/10453/34604