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This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: (i) the quantity of data that can be handled contemporarily is limited, due to the fact that reasoning is generally carried out in main-memory; (ii) the interaction with external (and independent) DBMSs is not(More)
—The availability of automatic tools for inferring semantics of database schemes is useful to solve several database design problems such as, that of obtaining Cooperative Information Systems or Data Warehouses from large sets of data sources. In this context, a main problem is to single out similarities or dissimilarities among scheme objects (interscheme(More)
Datalog ∃ is the extension of Datalog allowing existentially quantified variables in rule heads. This language is highly expressive and enables easy and powerful knowledge-modelling, but the presence of existentially quantified variables makes reasoning over Datalog ∃ undecidable in the general case. Restricted classes of Datalog ∃ , such as Shy, have been(More)
The task of an <i>information integration system</i> is to combine data residing at different sources, providing the user with a unified view of them, called <i>global schema.</i> Users formulate queries over the global schema, and the system suitably queries the sources, providing an answer to the user, who is not obliged to have any information about the(More)
In this paper we propose an approach to recommend to a user similar users, resources and social networks in a Social Internetworking Scenario. Our approach presents some interesting novelties with respect to the existing ones. First of all, it operates on a Social Internetworking context and not on a single social network. Moreover, it considers not only(More)