Efficient Processing of Metric Skyline Queries

Skyline query is of great importance in many applications, such as multicriteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (that is, their attribute vectors are fixed), whereas several recent works focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (that is, not limited to the euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries (MSQs) through a metric index. Most importantly, we formalize the query performance of the MSQ in terms of the pruning power (PP), by a cost model, in light of which we construct an optimized metric index aiming to maximize PP of MSQs. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering MSQs.