Il Distretto ICT in Sardegna e le attività previste sui contenuti digitali

2007-10-30Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaContenuti Digitali: una vera rivoluzione

Un'applicazione del framework MediaDART nell'ambito del Digital Asset Management

In questo lavoro viene presentato un possibile utilizzo del framework open source MediaDART nell'ambito del Digital Asset Management (DAM). MediaDART è un framework per la gestione di grosse collezioni di media basato su un'architettura scalabile e decentralizzata, composta da un numero arbitrario di nodi interconnessi da una rete p2p, che può crescere con il contributo degli utenti stessi. Il lavoro descrive inoltre il prototipo applicativo realizzato a scopo di valutazione e demo. Vengono infine descritte delle tematiche di ricerca aperte sul tema della classificazione collaborativa dei contenuti e vengono prospettati ulteriori sviluppi in termini di piattaforme di distribuzione.2008-03-17Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaPAAL 2008 - Pubblica Amministrazione Aperta e Libera: dalle tecnologie aperte alla libera circolazione dei contenuti digital

MediaDART: a decentralized framework for sharing multimedia content

This paper provides an overview of MediaDART, a framework for building online services for distributing and sharing digital media. Inspired by the participative model of Web 2.0, MediaDART relies on a scalable and decentralized architecture that can grow with the contribution of users. The architecture is based on an arbitrary number of nodes interconnected through a p2p network implementing a distributed hash table (DHT). The DHT provides resource storage and parallel resource processing for operations of feature extraction, adaptation and composition. MediaDART adopts application-level multicast based on distribution trees for delivery in streaming and implements algorithms to dynamically replicate resources across the network. The framework allows content description through user-defined tags. Tools for personalized content retrieval based on recommendation algorithms and user profiling are included too. This paper also describes two prototype applications and outlines further work.141-14

An ontology-based content management system for a dynamic operating context: issues and prototype evaluation

Content management systems are very useful tools for organizing and sharing information resources and may considerably benefit from using ontology-based description schemes. Ontologies set a common ground for resource acquisition, enabling different users to share a common view of a knowledge domain, and may considerably enhance the search paradigms by exploiting semantic relationships between concepts. However, ontologies may evolve since they reflect knowledge schemes that are by nature dynamic. Moreover this evolution should be the result of a collaborative process of ontology maintenance. These and other issues are addressed in the present work and some practical solutions are proposed. Also, a very simple prototype implementation of an ontology-based content management system is described. Finally, the results of a short experimentation of this prototype within a small community are presented

Group Recommendation with Automatic Identification of Users Communities

Recommender systems usually propose items to single users. However, in some domains like Mobile IPTV or Satellite Systems it might be impossible to generate a program schedule for each user, because of bandwidth limitations. A few approaches were proposed to generate group recommendations. However, these approaches take into account that groups of users already exist and no recommender system is able to detect intrinsic users communities. This paper describes an algorithm that detects groups of users whose preferences are similar and predicts recommendations for such groups. Groups of different granularities are generated through a modularity-based Community Detection algorithm, making it possible for a content provider to explore the trade off between the level of personalization of the recommendations and the number of channels. Experimental results show that the quality of group recommendations increases linearly with the number of groups created