k -dominant skyline queries on incomplete data

摘要: The skyline query has been extensively explored as one of popular techniques to filter uninteresting data objects, which plays an important role in many real-life applications such multi-criteria decision making and personalized services. This also incorporated into commercial database systems for supporting preference queries. However, a may retrieve too objects analyze intensively especially high-dimensional datasets. As result, k-dominant introduced control the number retrieved. Existing algorithms queries only aim at complete data, is not well-suited incomplete even though pervasive scientific research real life, due delivery failure, no power battery, accidental loss, etc. In this paper, we systematically study problem on (IkDS), where might miss their attribute values. We formalize IkDS then present three efficient finding over data. Several novel concepts/techniques are utilized including local skyline, dominance ability, bitmap index shrink search space. addition, extend our tackle two interesting variants, i.e., weighted dominant top-ź query, Extensive experiments using both synthetic sets demonstrate performance proposed algorithms.