FOPA: A Final Object Pruning Algorithm to Efficiently Produce Skyline Points

摘要: We consider the problem of locating best points in large multidimensional datasets. The goal is to efficiently generate all that meet a multi-objective query on data distributed Vertically Partitioned Tables (VPTs). To compute skyline VPTs, costly joins and comparisons may need be executed, negatively impacting execution time. propose new algorithm named FOPA (Final Object Pruning Algorithm) which able produce whole set scales up relies ordered information values seen so far, indices prune space dominated identify for datasets less time than state-of-the-art approaches. Empirically, we study performance scalability synthetic compare with existing approaches; our results suggest outperforms solutions by two orders magnitude.