5Ws model for big data analysis and visualization

Zhang, J; Huang, ML

5Ws model for big data analysis and visualization

Zhang, J

Huang, ML

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, 2013, pp. 1021 - 1028
Issue Date:: 2013-12-01

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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.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, 2013, pp. 1021 - 1028	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28352
dc.description.abstract	Big Data, which contains image, video, text, audio and other forms of data, collected from multiple datasets, is difficult to process using traditional database management tools or applications. In this paper, we establish the 5Ws model by using 5Ws data dimension for Big Data analysis and visualization. 5Ws data dimension stands for, What the data content is, Why the data occurred, Where the data came from, When the data occurred, Who received the data and How the data was transferred. This framework not only classifies Big Data attributes and patterns, but also establishes density patterns that provide more analytical features. We use visual clustering to display data sending and receiving densities which demonstrate Big Data patterns. The model is tested by using the network security ISCX2012 dataset. The experiment shows that this new model with clustered visualization can be efficiently used for Big Data analysis and visualization. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013	en_US
dc.relation.isbasedon	10.1109/CSE.2013.149	en_US
dc.title	5Ws model for big data analysis and visualization	en_US
dc.type	Conference Proceeding
utslib.for	080403 Data Structures	en_US
dc.location.activity	Sydney, Australia	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:

Big Data, which contains image, video, text, audio and other forms of data, collected from multiple datasets, is difficult to process using traditional database management tools or applications. In this paper, we establish the 5Ws model by using 5Ws data dimension for Big Data analysis and visualization. 5Ws data dimension stands for, What the data content is, Why the data occurred, Where the data came from, When the data occurred, Who received the data and How the data was transferred. This framework not only classifies Big Data attributes and patterns, but also establishes density patterns that provide more analytical features. We use visual clustering to display data sending and receiving densities which demonstrate Big Data patterns. The model is tested by using the network security ISCX2012 dataset. The experiment shows that this new model with clustered visualization can be efficiently used for Big Data analysis and visualization. © 2013 IEEE.

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http://hdl.handle.net/10453/28352