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An Intelligent PubSub Filtering System

Zeinah Hmedeh 1 Cedric Du Mouza 2 Nicolas Travers 1 
1 CEDRIC - VERTIGO - CEDRIC. Données complexes, apprentissage et représentations
CEDRIC - Centre d'études et de recherche en informatique et communications
2 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Content syndication has become a popular mean for timely delivery of frequently updated information on the Web. It essentially enhances traditional pull-oriented searching and browsing of web pages with push-oriented protocols. In such paradigm, publishers deliver brief information summaries on the Web, called news items, while information consumers subscribe to a number of feeds seen as information channels and get informed about the addition of recent items. However, many Web syndication applications imply a tight coupling between feed producers with consumers, and, they do not help users finding news items with interesting content. This demonstration shows a prototype which integrates the whole processus of thin filtering steps on numerous RSS feeds in memory, based on keyword-based subscriptions. Our system proposes to notify items related to set of keywords either by broad-match semantic, partial matching and a diversity/novelty filtering. In this demonstrate we discuss how our system integrates the three paradigms through dedicated indexes and a window-based structure for diversity/novelty filtering. We will demonstrate the management of notifications and global information on our system.
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Submitted on : Thursday, December 11, 2014 - 8:45:50 AM
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  • HAL Id : hal-01093708, version 1



Zeinah Hmedeh, Cedric Du Mouza, Nicolas Travers. An Intelligent PubSub Filtering System. Bases de Données Avancées, Oct 2013, Nantes, France. ⟨hal-01093708⟩



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