2D approach measuring multidimensional data pattern in big data visualization

Zhang, J; Huang, ML

2D approach measuring multidimensional data pattern in big data visualization

Zhang, J

Huang, ML

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, 2016
Issue Date:: 2016-07-12

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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.available	2016-03-12	en_US
dc.date.issued	2016-07-12	en_US
dc.identifier.citation	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, 2016	en_US
dc.identifier.isbn	9781467395908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43691
dc.description.abstract	© 2016 IEEE. Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen threeissues: 1) how to measure the structured and unstructured multidimensional data patterns for Big Data analysis; 2) how to display multidimensional data patterns in normal size of screen; 3) how to optimize the data attributes in Big Data visualization. In this work, we have visual analyzed Big Datavariety based on the complexity of multidimensional data. Firstly, we introduce2D dimension which divided the multidimensional dataset into 2D data pattern subsets, and then establish 2D-Ratio algorithm to measure 2D dimension in multiple data patterns. Second, we createtwo additional parallel axes by using 2D-Ratio to compare 2D dimensional patterns for visualization. Third, the dimension clustering and shrunk attribute have been introduced in 2D-Ratio parallel coordinates to reduce the data over-crowed. The experiment shows that our model can be efficiently and accurately used for Big Data analysis and visualization.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP0669645
dc.relation.ispartof	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016	en_US
dc.relation.isbasedon	10.1109/ICBDA.2016.7509823	en_US
dc.title	2D approach measuring multidimensional data pattern in big data 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
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen threeissues: 1) how to measure the structured and unstructured multidimensional data patterns for Big Data analysis; 2) how to display multidimensional data patterns in normal size of screen; 3) how to optimize the data attributes in Big Data visualization. In this work, we have visual analyzed Big Datavariety based on the complexity of multidimensional data. Firstly, we introduce2D dimension which divided the multidimensional dataset into 2D data pattern subsets, and then establish 2D-Ratio algorithm to measure 2D dimension in multiple data patterns. Second, we createtwo additional parallel axes by using 2D-Ratio to compare 2D dimensional patterns for visualization. Third, the dimension clustering and shrunk attribute have been introduced in 2D-Ratio parallel coordinates to reduce the data over-crowed. The experiment shows that our model can be efficiently and accurately used for Big Data analysis and visualization.

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

http://hdl.handle.net/10453/43481