Qstack: Multi-tag Visual Rankings

Pham, PG; Huang, M

Qstack: Multi-tag Visual Rankings

Pham, PG

Huang, M

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Publisher:: Academy Publisher
Publication Type:: Journal Article
Citation:: Journal of Software, 2016, 11 (7), pp. 695 - 703 (9)
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Pham, PG https://orcid.org/0000-0003-0954-9959	en_US
dc.contributor.author	Huang, M https://orcid.org/0000-0002-6896-6480	en_US
dc.date.available	2016-02-20	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	Journal of Software, 2016, 11 (7), pp. 695 - 703 (9)	en_US
dc.identifier.issn	1796-217X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43918
dc.description.abstract	Multi-tag-based search is quite popular on collaborative websites and sharing-online-content systems. For this kind of search results, the challenge is how to compare grouped-tag values of tag collections on heterogeneous alternatives. This paper introduces a new visualization approach named Qstack for dealing with the challenge. Qstack purpose is to help users to visually rank multi-tags based on grouped-score combination within and across the categorized alternatives. The methodology applying interactive stacked bars, dynamic queries and adaptive focus+context techniques enables users to easily create and adjust grouped-tag rankings of a large number of heterogeneous alternatives. A case study on Flickr photo award allocation will be presented for Qstack demonstration. We conducted a qualitative study for evaluating Qstack effectiveness, and the result indicates that our approach is useful for multi-tag rankings.	en_US
dc.publisher	Academy Publisher	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP0669645
dc.relation.ispartof	Journal of Software	en_US
dc.relation.isbasedon	10.17706/jsw.11.7.695-703	en_US
dc.title	Qstack: Multi-tag Visual Rankings	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	11	en_US
utslib.for	0803 Computer Software	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/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.consider-herdc	true	en_US
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

Multi-tag-based search is quite popular on collaborative websites and sharing-online-content systems. For this kind of search results, the challenge is how to compare grouped-tag values of tag collections on heterogeneous alternatives. This paper introduces a new visualization approach named Qstack for dealing with the challenge. Qstack purpose is to help users to visually rank multi-tags based on grouped-score combination within and across the categorized alternatives. The methodology applying interactive stacked bars, dynamic queries and adaptive focus+context techniques enables users to easily create and adjust grouped-tag rankings of a large number of heterogeneous alternatives. A case study on Flickr photo award allocation will be presented for Qstack demonstration. We conducted a qualitative study for evaluating Qstack effectiveness, and the result indicates that our approach is useful for multi-tag rankings.

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

http://hdl.handle.net/10453/43918