Big data density analytics using parallel coordinate visualization

Zhang, J; Huang, ML; Wang, WB; Lu, LF; Meng, ZP

Big data density analytics using parallel coordinate visualization

Zhang, J

Huang, ML

Wang, WB Lu, LF Meng, ZP

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014, 2015, pp. 1115 - 1120
Issue Date:: 2015-01-26

Closed Access

	Filename	Description	Size
	2014-ISPAN.pdf	Published version	965.38 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Wang, WB	en_US
dc.contributor.author	Lu, LF	en_US
dc.contributor.author	Meng, ZP	en_US
dc.date.issued	2015-01-26	en_US
dc.identifier.citation	Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014, 2015, pp. 1115 - 1120	en_US
dc.identifier.isbn	9781479979813	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35377
dc.description.abstract	© 2014 IEEE. Parallel coordinate is a popular tool for visualizing high-dimensional data and analyzing multivariate data. With the rapid growth of data size and complexity, data clutter in parallel coordinates is a major issue for Big Data visualization. This has given rise to three problems; 1) how to rearrange the parallel axes without the loss of data patterns, 2) how to shrink data attributes on each axis without the loss of data trends, 3) how to visualize the structured and unstructured data patterns for Big Data analysis. In this paper, we introduce the 5Ws dimensions as the parallel axes and establish the 5Ws sending density and receiving density as additional axes for Big Data visualization. Our model not only demonstrates Big Data attributes and patterns, but also reduces data over-lapping by up to 80 percent without the loss of data patterns. Experiments show that this new model can be efficiently used for Big Data analysis and visualization.	en_US
dc.relation.ispartof	Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014	en_US
dc.relation.isbasedon	10.1109/CSE.2014.219	en_US
dc.title	Big data density analytics using parallel coordinate visualization	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Parallel coordinate is a popular tool for visualizing high-dimensional data and analyzing multivariate data. With the rapid growth of data size and complexity, data clutter in parallel coordinates is a major issue for Big Data visualization. This has given rise to three problems; 1) how to rearrange the parallel axes without the loss of data patterns, 2) how to shrink data attributes on each axis without the loss of data trends, 3) how to visualize the structured and unstructured data patterns for Big Data analysis. In this paper, we introduce the 5Ws dimensions as the parallel axes and establish the 5Ws sending density and receiving density as additional axes for Big Data visualization. Our model not only demonstrates Big Data attributes and patterns, but also reduces data over-lapping by up to 80 percent without the loss of data patterns. Experiments show that this new model can be efficiently used for Big Data analysis and visualization.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/35377