Research on K Nearest Neighbor Skyline Query in Time Dependent Road Network

Abstract

Abstract In recent years, with the increase for query preference, skyline query in road networks has gradually become a research hot topic. In order to help users get the desired query result, we propose a new algorithm. Firstly, we select landmark heuristically and establish corresponding shortest path tree (SPT). Then we construct a skyline matrix index called time dependent matrix index (TDMI), which help us associate the updated result set with dynamic k nearest neighbor (KNN) and simplify the non-space dominance relationship better. Finally, with SPT, TDMI, KNN and dynamic pruning conditions, an algorithm, the TDMI-KS algorithm, is proposed to rapidly answer the query result. Extensive experiments using Oldenburg road network datasets demonstrate the effectiveness and the efficiency of our proposed TDMI-KS algorithms.