G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks

Abstract

In the recent decades, we have witnessed the rapidly growing popularity of location-based systems. Three types of location-based queries on road networks, single-pair shortest path query, $k$ nearest neighbor ( $k$ NN) query, and keyword-based $k$ NN query, are widely used in location-based systems. Inspired by $\tt R$ - $\tt tree$ , we propose a height-balanced and scalable index, namely $\tt G$ - $\tt tree$ , to efficiently support these queries. The space complexity of $\tt G$ - $\tt tree$ is $\mathcal {O}(|\mathcal {V}|\log {|\mathcal {V}|})$ where ${|\mathcal {V}|}$ is the number of vertices in the road network. Unlike previous works that support these queries separately, $\tt G$ - $\tt tree$ supports all these queries within one framework. The basis for this framework is an assembly-based method to calculate the shortest-path distances between two vertices. Based on the assembly-based method, efficient search algorithms to answer $k$ NN queries and keyword-based $k$ NN queries are developed. Experiment results show $\tt G$ - $\tt tree$ ’s theoretical and practical superiority over existing methods.