efficient top-k retrieval for user preference queries
Efficient retrieval of the most relevant (i.e. top-k) tuples is an important requirement in information systems which access large amounts of data. In general answering a top-k query request means to retrieve the k-objects which score best for an objective function given with an input query. Such queries are frequent where users specify a set of restrictions defining their ideal solution and want to retrieve results which are closest to these ideals. Within this work we show how the well known Threshold Algorithm (short TA) of Fagin et al. [8] can be improved both in time and memory requirements. We do so by dynamically creating intelligent index structures out of the query restrictions posed by the user. The further we present a powerful extension: user preference queries where weighted preferences on query restrictions influence the objective function used to select the top-k objects from a relation or a database. Therefore we integrated the capability to deal with user preference queries into our top-k query answering approach. Users may define restrictions, assign weights and retrieve the top-k objects matching the given preferences best. We prototypically implemented our approach and evaluated it by validating its results against the results achieved with TA.
user preference queries top-k query answering result ranking approximate querying