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Bichromatic reverse nearest neighbor (BRNN) has been extensively studied in spatial database literature. While previous algorithms for BRNN queries rely mainly on in-memory and do not guarantee the scalability. A straightforward approach is to determine the BRNN for all possible points that are not feasible since there are a large or infinite number of possible points. To the best of our knowledge,...
Dynamic skyline queries are useful in decision making and data-intensive applications. With the number of data increases, such skyline calculation is a challenging problem. Unfortunately, skyline query processing in centralized system is not suitable for the case. In this paper we propose a parallel algorithm which can calculate dynamic skylines with MapReduce. Firstly, we build an appropriate Inverted...
Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed...
Reverse k Nearest Neighbor (RkNN) queries are of particular interest in a wide range of data mining applications such as decision support systems, profile based marketing and spatial database etc. With the increasing volume of spatial data, it is difficult to perform RkNN queries efficiently because of the limited computational capability and storage resources. In this paper, we investigate how to...
In this paper, a fast method of identifying the vulnerable sections in power system based on the graph theory is proposed. First, several kinds of definition of weights for power grid modeling are discussed and a novel index which can consider the electrical distance and the power flow distribution at the same time is defined. Then, a weighted graph model of the power grid is established based on...
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