Online hot event discovery based on Association Link Network

Liu, Y; Luo, X; Xuan, J

Online hot event discovery based on Association Link Network

Liu, Y Luo, X Xuan, J

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Publication Type:: Journal Article
Citation:: Concurrency Computation, 2015, 27 (15), pp. 4001 - 4014
Issue Date:: 2015-01-01

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	Liu_et_al-2015-Concurrency_and_Computation__Practice_and_Experience.pdf	Published Version	1.21 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Liu, Y	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Concurrency Computation, 2015, 27 (15), pp. 4001 - 4014	en_US
dc.identifier.issn	1532-0626	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120523
dc.description.abstract	© 2015 John Wiley & Sons, Ltd. Online hot event discovery has become a flourishing frontier where online document streams are monitored to discover newly occurring events or assigned to previously detected events. However, hot events have the nature to evolve, and their inherent topically related words are also likely to evolve. It makes event discovery a challenging task for traditional-mining approaches. Combining word association and semantic community, Association Link Network (ALN) organizes the loosely distributed associated resources. This paper presents an ALN-based novel online hot event discovery approach. Technically, this approach is enacted around three stages. In the first stage, we extract significant features to represent the content of each document from the online document stream. During the second stage, we classify the online document stream into topically related detected events considering event evolution in the form of ALN. At the third stage, we create an ALN-based event detection algorithm, which is used to timely discover newly occurring hot events. The online datasets used in our empirical studies are acquired from Baidu News, which spans a range of 1315 hot events and 236,300 documents. Experimental results demonstrate the hot events discovery ability with respect to high accuracy, good scalability, and short runtime.	en_US
dc.relation.ispartof	Concurrency Computation	en_US
dc.relation.isbasedon	10.1002/cpe.3374	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Online hot event discovery based on Association Link Network	en_US
dc.type	Journal Article
utslib.citation.volume	15	en_US
utslib.citation.volume	27	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.issue	15	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 2015 John Wiley & Sons, Ltd. Online hot event discovery has become a flourishing frontier where online document streams are monitored to discover newly occurring events or assigned to previously detected events. However, hot events have the nature to evolve, and their inherent topically related words are also likely to evolve. It makes event discovery a challenging task for traditional-mining approaches. Combining word association and semantic community, Association Link Network (ALN) organizes the loosely distributed associated resources. This paper presents an ALN-based novel online hot event discovery approach. Technically, this approach is enacted around three stages. In the first stage, we extract significant features to represent the content of each document from the online document stream. During the second stage, we classify the online document stream into topically related detected events considering event evolution in the form of ALN. At the third stage, we create an ALN-based event detection algorithm, which is used to timely discover newly occurring hot events. The online datasets used in our empirical studies are acquired from Baidu News, which spans a range of 1315 hot events and 236,300 documents. Experimental results demonstrate the hot events discovery ability with respect to high accuracy, good scalability, and short runtime.

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http://hdl.handle.net/10453/120523