Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
