----------------------------------------------------------------------------- DATABASE SEMINAR ----------------------------------------------------------------------------- Davide Martinenghi, Politecnico di Milano ----------------------------------------------------------------------------- Humans Fighting Uncertainty in Top-K Scenarios ----------------------------------------------------------------------------- Martedi', 9 luglio, 2013 -- h 12:00 **** Sala riunioni **** Dipartimento di Ingegneria Sezione di Informatica e Automazione Universita' Roma Tre Via Vasca Navale, 79 primo piano ----------------------------------------------------------------------------- ABSTRACT Finding the best answers to a query is a problem of paramount importance in many scenarios, including big data analysis, Web queries, and several other data-centric contexts. A common hindrance to this task comes from an inherent amount of uncertainty that may reside both in the data at hand (e.g., due to unreliability of data sources) and in the query (e.g., in the relative importance of some attributes of the queried sources). Uncertain answers entail uncertain ranking, i.e., there is no consensus on how to rank the tuples in the query answer. One way to cope with this problem is to determine the most representative ranking out of the possible rankings compatible with an uncertain scenario. Orthogonally, one can also try to reduce the amount of uncertainty by asking questions to human users in order to disambiguate the mutual order of some answer tuples. After discussing top-K query answering in the presence of uncertainty, we shall illustrate suitable strategies for exploiting the availability of a crowd of humans by determining which questions to ask and which users to select. -----------------------------------------------------------------------------