Incremental evaluation of top-k combinatorial metric skyline query

In this paper, we define a novel type of skyline query, namely top-k combinatorial metric skyline (kCMS) query. The kCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The kCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the kCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.