Mining websites preferences on web events in big data environment

Xuan, J; Luo, X; Lu, J

Mining websites preferences on web events in big data environment

Xuan, J

Luo, X Lu, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, 2013, pp. 1043 - 1050
Issue Date:: 2013-12-01

Closed Access

	Filename	Description	Size
	2013003239OK.pdf		963.67 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	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	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. 1043 - 1050	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28343
dc.description.abstract	On the web, there are numerous websites publishing web pages to cover the events occurring in society. The web events data satisfies the well-accepted attributes of big data: Volume, Velocity, Variety and Value. As a great value of web events data, website preferences can help the followers of web events, e.g. peoples or organizations, to select the proper websites to follow their interested aspects of web events. However, the big volume, fast evolution speed, multisource and unstructured data all together make the value of website preferences mining very challenging. In this paper, website preference is formally defined at first. Then, according to the hierarchical attribute of web events data, we propose a hierarchical network model to organize big data of a web event from different organizations, different areas and different nations at a given time stamp. With this hierarchical network structure in hand, two strategies are proposed to mine the value of websites preferences from web events data. The first straightforward strategy utilizes the communities of keyword level network and the mapping relations between websites and keywords to unveil the Value in them. By taking the whole hierarchical network structure into consideration, an iterative algorithm is proposed in second strategy to refine the keyword communities like the first strategy. At last, an evaluation criteria of website preferences is designed to compare the performances of two proposed strategies. Experimental results show the proper combination of horizontal relations (each level network) with vertical relations (mapping relations between three level networks) can extract more value from web events data and then improve the efficiency on website preferences mining. © 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.2013.152	en_US
dc.title	Mining websites preferences on web events in big data environment	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

On the web, there are numerous websites publishing web pages to cover the events occurring in society. The web events data satisfies the well-accepted attributes of big data: Volume, Velocity, Variety and Value. As a great value of web events data, website preferences can help the followers of web events, e.g. peoples or organizations, to select the proper websites to follow their interested aspects of web events. However, the big volume, fast evolution speed, multisource and unstructured data all together make the value of website preferences mining very challenging. In this paper, website preference is formally defined at first. Then, according to the hierarchical attribute of web events data, we propose a hierarchical network model to organize big data of a web event from different organizations, different areas and different nations at a given time stamp. With this hierarchical network structure in hand, two strategies are proposed to mine the value of websites preferences from web events data. The first straightforward strategy utilizes the communities of keyword level network and the mapping relations between websites and keywords to unveil the Value in them. By taking the whole hierarchical network structure into consideration, an iterative algorithm is proposed in second strategy to refine the keyword communities like the first strategy. At last, an evaluation criteria of website preferences is designed to compare the performances of two proposed strategies. Experimental results show the proper combination of horizontal relations (each level network) with vertical relations (mapping relations between three level networks) can extract more value from web events data and then improve the efficiency on website preferences mining. © 2013 IEEE.

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

http://hdl.handle.net/10453/28343