Research on Continuous k Nearest Neighbor Queries in Road Networks
Presented a novel directional graph model for efficient processing of continuous k nearest neighbor (k-NN) queries in road networks.We use in-memory hash table and linear list structures to store the moving objects and describe the directional model graph respectively.By introducing unidirectional network distance and bidirectional network distance metrics,we propose the unidirectional network expansion algorithm and bidirectional network expansion algorithm to support different semantic continuous k-NN queries,and use influence tree and network expansion strategy to reduce the network searching cost of continuous k-NN queries updating.Experimental results show that the above two algorithms outperforms existing algorithms including IMA and MKNN algorithms.