Skyline Computation Based on Previously Computed Results

Abstract

Many methods are used to retrieve relevant information in big data. One of these is the Skyline operator, which is used to retrieve the best objects in multidimensional datasets. The Skyline result helps to extract the required data with the optimal combination of characteristics of the data efficiently. In real big data, the data is often updated, and new data can be added deleted, or updated. A required recomputation of the Skyline each time the data is updated may lead to unacceptable response time. In this paper, we focus on reducing the Skyline recomputation time every time the dataset is updated. We proposed an approach that benefits from the overlap of precomputed Skyline results. And for this purpose, we used the history of Skyline computation results to recompute the new Skyline after updating the data. Based on the experiments we have performed; our approach can significantly reduce the Skyline recomputation time every time the data is updated.