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Online Change Estimation Models for Dynamic Web Resources

Roxana Horincar 1 Bernd Amann 1 Thierry Artières 2
1 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Modern web 2.0 applications have transformed the Internet into an interactive, dynamic and alive information space. Personal weblogs, commercial web sites, news portals and social media applications generate highly dynamic information streams which have to be propagated to millions of users. This article focuses on the problem of estimating the publication frequency of highly dynamic web resources. We illustrate the importance of developing efficient online estimation techniques for improving the refresh strategies of RSS feed aggregators like Google Reader [8], Datasift [7] or Roses [11]. We study the temporal publication characteristics of a large collection of real world RSS feeds and we define and evaluate several online estimation methods in cohesion with different refresh strategies. We show the benefit of using periodical source publication patterns for change estimation and we highlight the challenges imposed by the application context.
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Submitted on : Friday, February 5, 2016 - 4:05:07 PM
Last modification on : Friday, January 8, 2021 - 5:34:11 PM

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Roxana Horincar, Bernd Amann, Thierry Artières. Online Change Estimation Models for Dynamic Web Resources. 12th International Conference on Web Engineering (ICWE), Jul 2012, Berlin, Germany. pp.395-410, ⟨10.1007/978-3-642-31753-8_33⟩. ⟨hal-01270082⟩



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