Efficient continuous skyline computation on multi-core processors based on Manhattan distance

The continuous Skyline query has recently become the subject of the several researches due to its wide spectrum of applications such as multi-criteria decision making, graph analysis network, wireless sensor network and data exploration. In these applications, the datasets are huge and have various dimensions. Moreover, they constantly change as time passes. Therefore, this query is considered as a computation intensive operation that finding the result in a reasonable time is a challenge. In this paper, we present an efficient parallel continuous Skyline approach. In our suggested method, the dataset points are sorted and pruned based on Manhattan distance. Moreover, we use several optimization methods to optimize memory usage in comparison with naïve implementation. In addition, besides the applied conventional parallelization methods, we partition the time steps based on the number of available cores. The experimental results for a dataset that contains 800k points with 7 dimensions show considerable speedup.