Visual analytics for BigData variety and its behaviours

Zhang, J; Huang, ML; Meng, ZP

Visual analytics for BigData variety and its behaviours

Zhang, J

Huang, ML

Meng, ZP

Permalink

Publication Type:: Journal Article
Citation:: Computer Science and Information Systems, 2015, 12 (4), pp. 1127 - 1191
Issue Date:: 2015-01-01

Closed Access

	Filename	Description	Size
	CompSIS_4.pdf	Published Version	2.1 MB	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	Meng, ZP	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Computer Science and Information Systems, 2015, 12 (4), pp. 1127 - 1191	en_US
dc.identifier.issn	1820-0214	en_US
dc.identifier.uri	http://hdl.handle.net/10453/38187
dc.description.abstract	© 2015 ComSIS Consortium. All rights reserved. BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the „5Ws‟ dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the „5Ws‟ densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the „5Ws‟ sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.	en_US
dc.relation.ispartof	Computer Science and Information Systems	en_US
dc.relation.isbasedon	10.2298/CSIS141122050Z	en_US
dc.title	Visual analytics for BigData variety and its behaviours	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	12	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0899 Other Information and Computing Sciences	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

© 2015 ComSIS Consortium. All rights reserved. BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the „5Ws‟ dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the „5Ws‟ densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the „5Ws‟ sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

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

http://hdl.handle.net/10453/38187