文章基本信息

标题：Efficient Shared Execution Processing of k-Nearest Neighbor Joins in Road Networks
本地全文：下载
作者：Hyung-Ju Cho
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2018
卷号：2018
DOI：10.1155/2018/1243289
出版社：Hindawi Publishing Corporation
摘要：We investigate the k-nearest neighbor (kNN) join in road networks to determine the k-nearest neighbors (NNs) from a dataset S to every object in another dataset R. The kNN join is a primitive operation and is widely used in many data mining applications. However, it is an expensive operation because it combines the kNN query and the join operation, whereas most existing methods assume the use of the Euclidean distance metric. We alternatively consider the problem of processing kNN joins in road networks where the distance between two points is the length of the shortest path connecting them. We propose a shared execution-based approach called the group-nested loop (GNL) method that can efficiently evaluate kNN joins in road networks by exploiting grouping and shared execution. The GNL method can be easily implemented using existing kNN query algorithms. Extensive experiments using several real-life roadmaps confirm the superior performance and effectiveness of the proposed method in a wide range of problem settings.