Efficient identification of local keyword patterns in microblogging platforms

Wang, X; Zhang, Y; Zhang, W; Lin, X

Efficient identification of local keyword patterns in microblogging platforms

Wang, X

Zhang, Y

Zhang, W Lin, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2016, 28 (10), pp. 2621 - 2634
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Wang, X https://orcid.org/0000-0003-3554-3219	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lin, X	en_US
dc.date.issued	2016-10-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2016, 28 (10), pp. 2621 - 2634	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/108265
dc.description.abstract	© 1989-2012 IEEE. Microblogging platforms, such as Twitter, serve as an important and efficient channel for sharing information. With the prevalence of geo-position enabled devices, a rapidly growing amount of microblogs are associated with geo-tags. Consequently, real-time analysis of the geo-tagged microblog stream has attracted great attentions. In this paper, we advocate the significance of keyword co-occurrence for geo-tagged microblogs analysis, which has been overlooked by existing studies. The co-occurrence of keywords is necessary to resolve the ambiguity in event analysis, especially when different events have overlapping descriptions. Given a geo-tagged microblog stream, we formally define the problem of identifying local (top-K ) maximal frequent keyword co-occurrence patterns over geo-tagged microblog stream, namely LFP (LKFP) query. Given a query region, LFP query aims to retrieve the local maximal keyword patterns with frequency exceeding a given threshold; while LKFP query aims to identify K maximal keyword patterns with highest local frequency, in case users do not have a threshold in mind. To handle the high volume microblog stream and meet the requirement when a large number of queries are issued, we develop novel data structures to maintain the data stream, and propose efficient algorithms to process LFP and LKFP queries with theoretical underpinnings. The extensive empirical study on real dataset confirms the effectiveness and efficiency of our approaches.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2016.2578330	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Efficient identification of local keyword patterns in microblogging platforms	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	28	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	08 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/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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 1989-2012 IEEE. Microblogging platforms, such as Twitter, serve as an important and efficient channel for sharing information. With the prevalence of geo-position enabled devices, a rapidly growing amount of microblogs are associated with geo-tags. Consequently, real-time analysis of the geo-tagged microblog stream has attracted great attentions. In this paper, we advocate the significance of keyword co-occurrence for geo-tagged microblogs analysis, which has been overlooked by existing studies. The co-occurrence of keywords is necessary to resolve the ambiguity in event analysis, especially when different events have overlapping descriptions. Given a geo-tagged microblog stream, we formally define the problem of identifying local (top-K ) maximal frequent keyword co-occurrence patterns over geo-tagged microblog stream, namely LFP (LKFP) query. Given a query region, LFP query aims to retrieve the local maximal keyword patterns with frequency exceeding a given threshold; while LKFP query aims to identify K maximal keyword patterns with highest local frequency, in case users do not have a threshold in mind. To handle the high volume microblog stream and meet the requirement when a large number of queries are issued, we develop novel data structures to maintain the data stream, and propose efficient algorithms to process LFP and LKFP queries with theoretical underpinnings. The extensive empirical study on real dataset confirms the effectiveness and efficiency of our approaches.

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http://hdl.handle.net/10453/108265