Several operations of Web-based applications areoptimized with respect to the set of resources that will receivethe majority of requests in the near future, namely the hotset. Unfortunately, the existing algorithms for the hot setidentification do not work well for the emerging social networkapplications, that are characterized by quite novel featureswith respect to the traditional Web: highly interactive useraccesses, upload and download operations, short lifespan ofthe resources, social interactions among the members of theonline communities.We propose and evaluate innovative combinations of predictivemodels and social-aware solutions for the identificationof the hot set. Experimental results demonstrate that some ofthe considered algorithms improve the accuracy of the hot setidentification up to 30% if compared to existing models, andthey guarantee stable and robust results even in the context ofsocial network applications characterized by high variability.
Hot set identification for social network applications / Canali, Claudia; Colajanni, Michele; Lancellotti, Riccardo. - STAMPA. - n/a:(2009), pp. 280-285. (Intervento presentato al convegno 2009 33rd Annual IEEE International Computer Software and Applications Conference, COMPSAC 2009 tenutosi a Seattle, WA, usa nel July 20-July 24 2009) [10.1109/COMPSAC.2009.44].
Hot set identification for social network applications
CANALI, Claudia;COLAJANNI, Michele;LANCELLOTTI, Riccardo
2009
Abstract
Several operations of Web-based applications areoptimized with respect to the set of resources that will receivethe majority of requests in the near future, namely the hotset. Unfortunately, the existing algorithms for the hot setidentification do not work well for the emerging social networkapplications, that are characterized by quite novel featureswith respect to the traditional Web: highly interactive useraccesses, upload and download operations, short lifespan ofthe resources, social interactions among the members of theonline communities.We propose and evaluate innovative combinations of predictivemodels and social-aware solutions for the identificationof the hot set. Experimental results demonstrate that some ofthe considered algorithms improve the accuracy of the hot setidentification up to 30% if compared to existing models, andthey guarantee stable and robust results even in the context ofsocial network applications characterized by high variability.
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