Abstract
Skyline query function is one of promising information filtering methods. Skyline queries return a set of interesting data objects that are not dominated by any other object on all dimensions. Therefore in this paper, we consider k-dominant skyline computation when the underlying dataset is partitioned into geographically distant computing core that are connected to the coordinator (server). The existing solutions are not suitable for our problem, because they are restricted to centralized query processors, limiting scalability and imposing a single point of failure. In this paper, we developed a distributed k-dominant skyline queries (DKSQ) computation algorithm. Where the coordinator iteratively transmits data to each computing core. Computing core is able to prune a large amount of local data, which otherwise would need to be sent to the coordinator. Extensive performance study shows that proposed algorithm is efficient and robust to different data distributions.
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