Towards Efficient K-Dominant Skyline Computation in CSCW

Considering benefits of different collaborators, cooperative work usually faces the challenge to make a trade-off decision upon multiple criterions. K-dominant skyline, a novel database query technology, may return appropriate optimal choices and play as a solution. To reduce intensive comparisons between objects in skyline computation, we propose a novel sorting-based algorithm named Sorted cumulative algorithm. Furthermore, to meet the requirement of distributed collaborative environment, we also address Parallel SCA, which accelerates the computation based on some data partitioning techniques. Extensive experiments are conducted to confirm the superior efficiency of the proposed methods against existing ones, especially in the context of high dimensional datasets.