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

• Published in: IEEE transactions on knowledge and data engineering Vol. 27; no. 8; pp. 2175 - 2189
• Main Authors: Zhong, Ruicheng, Li, Guoliang, Tan, Kian-Lee, Zhou, Lizhu, Gong, Zhiguo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Complexity theory; Educational institutions; Index; Indexes; Keyword Search; KNN Search; Mathematical analysis; Neural networks; Partitioning algorithms; Queries; Road Network; Roads; Search algorithms; Search problems; Searching; Shortest-path problems; Single-Pair Shortest Path; Spatial Databases; Transmission line matrix methods; Trees; index; keyword search; road network; KNN search; spatial databases; Single-pair shortest path
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2015.2399306

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 (kNN) query, and keyword-based kNN query, are widely used in location-based systems. Inspired by R-tree, we propose a height-balanced and scalable index, namely G-tree, to efficiently support these queries. The space complexity of G-tree is O(|V|log|V|) where |V| is the number of vertices in the road network. Unlike previous works that support these queries separately, G-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 kNN queries and keyword-based kNN queries are developed. Experiment results show G-tree's theoretical and practical superiority over existing methods.