This article 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, because reasoning is generally carried out in main-memory; (ii) the interaction with external (and independent) Database Management Systems is not trivial and, in several cases, not allowed at all; and (iii) the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This article provides a contribution in this setting; it presents a new system, called DLVDB, which aims to solve these problems. Moreover, it reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are LDL++, XSB, Smodels, and three top-level commercial Database Management Systems. DLVDB significantly outperforms even the commercial database systems on recursive queries.
