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Article: Redips: Backlink search and analysis on the web for business intelligence analysis

TitleRedips: Backlink search and analysis on the web for business intelligence analysis
Authors
Issue Date2007
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.html
Citation
Journal Of The American Society For Information Science And Technology, 2007, v. 58 n. 3, p. 351-365 How to Cite?
AbstractThe World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of interest) can provide important insights about the company's "online communities." Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business intelligence analysis methods - using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures. © 2006 Wiley Periodicals, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/85895
ISSN
2015 Impact Factor: 2.452
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChau, Men_HK
dc.contributor.authorShiu, Ben_HK
dc.contributor.authorChan, Ien_HK
dc.contributor.authorChen, Hen_HK
dc.date.accessioned2010-09-06T09:10:29Z-
dc.date.available2010-09-06T09:10:29Z-
dc.date.issued2007en_HK
dc.identifier.citationJournal Of The American Society For Information Science And Technology, 2007, v. 58 n. 3, p. 351-365en_HK
dc.identifier.issn1532-2882en_HK
dc.identifier.urihttp://hdl.handle.net/10722/85895-
dc.description.abstractThe World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of interest) can provide important insights about the company's "online communities." Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business intelligence analysis methods - using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures. © 2006 Wiley Periodicals, Inc.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.htmlen_HK
dc.relation.ispartofJournal of the American Society for Information Science and Technologyen_HK
dc.rightsJournal of the American Society for Information Science and Technology. Copyright © John Wiley & Sons, Inc.en_HK
dc.titleRedips: Backlink search and analysis on the web for business intelligence analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1532-2882&volume=58&spage=351&epage=365&date=2007&atitle=Redips:+Backlink+Search+and+Analysis+on+the+Web+for+Business+Intelligence+Analysisen_HK
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_HK
dc.identifier.authorityChau, M=rp01051en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/asi.20503en_HK
dc.identifier.scopuseid_2-s2.0-33847744711en_HK
dc.identifier.hkuros137551en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33847744711&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue3en_HK
dc.identifier.spage351en_HK
dc.identifier.epage365en_HK
dc.identifier.isiWOS:000246379400004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChau, M=7006073763en_HK
dc.identifier.scopusauthoridShiu, B=16032328400en_HK
dc.identifier.scopusauthoridChan, I=16030446000en_HK
dc.identifier.scopusauthoridChen, H=35213102500en_HK

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