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Novel approaches to crawling important pages early

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dc.contributor.authorAlam, Md. Hijbul-
dc.contributor.authorHa, JongWoo-
dc.contributor.authorLee, SangKeun-
dc.date.accessioned2021-09-06T12:31:48Z-
dc.date.available2021-09-06T12:31:48Z-
dc.date.created2021-06-14-
dc.date.issued2012-12-
dc.identifier.issn0219-1377-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/106789-
dc.description.abstractWeb crawlers are essential to many Web applications, such as Web search engines, Web archives, and Web directories, which maintain Web pages in their local repositories. In this paper, we study the problem of crawl scheduling that biases crawl ordering toward important pages. We propose a set of crawling algorithms for effective and efficient crawl ordering by prioritizing important pages with the well-known PageRank as the importance metric. In order to score URLs, the proposed algorithms utilize various features, including partial link structure, inter-host links, page titles, and topic relevance. We conduct a large-scale experiment using publicly available data sets to examine the effect of each feature on crawl ordering and evaluate the performance of many algorithms. The experimental results verify the efficacy of our schemes. In particular, compared with the representative RankMass crawler, the FPR-title-host algorithm reduces computational overhead by a factor as great as three in running time while improving effectiveness by 5 % in cumulative PageRank.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSPRINGER LONDON LTD-
dc.titleNovel approaches to crawling important pages early-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, SangKeun-
dc.identifier.doi10.1007/s10115-012-0535-4-
dc.identifier.scopusid2-s2.0-84869092092-
dc.identifier.wosid000310871700009-
dc.identifier.bibliographicCitationKNOWLEDGE AND INFORMATION SYSTEMS, v.33, no.3, pp.707 - 734-
dc.relation.isPartOfKNOWLEDGE AND INFORMATION SYSTEMS-
dc.citation.titleKNOWLEDGE AND INFORMATION SYSTEMS-
dc.citation.volume33-
dc.citation.number3-
dc.citation.startPage707-
dc.citation.endPage734-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.subject.keywordAuthorWeb crawler-
dc.subject.keywordAuthorCrawl ordering-
dc.subject.keywordAuthorPageRank-
dc.subject.keywordAuthorFractional PageRank-
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