Range-Based Nearest Neighbor Queries with Complex-Shaped Obstacles

• Published in: IEEE transactions on knowledge and data engineering Vol. 30; no. 5; pp. 963 - 977
• Main Authors: Zhu, Huaijie, Yang, Xiaochun, Wang, Bin, Lee, Wang-Chien
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithm design and analysis; Algorithms; Artificial neural networks; Barriers; Division; Indexes; Mobile communication; Nearest neighbor; obstacle; Performance evaluation; Queries; Query processing; range-based nearest neighbor; Spatial databases
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2017.2779487

In this paper, we study a novel variant of obstructed nearest neighbor queries, namely, range-based obstructed nearest neighbor(RONN) search. As a natural generalization of continuous obstructednearest-neighbor(CONN), an RONN query retrieves a set of obstructed nearest neighbors corresponding to every point in a specified range. We propose a new index, namely binary obstructed tree (called OB-tree), for indexing complex objects in the obstructed space. The novelty of OB-tree lies in the idea of dividing the obstructed space into non-obstructedsubspaces, aiming to efficiently retrieve highly qualified candidates for RONN processing. We develop an algorithm for construction of the OB-tree and propose a space division scheme, called optimal obstacle balance (OOB2) scheme, to address the tree balance problem. Accordingly, we propose an efficient algorithm, called RONN by OB-tree Acceleration (RONN-OBA), which exploits the OB-tree and a binary traversal order of data objects to accelerate query processing of RONN. In addition, we extend our work in several aspects regarding the shape of obstacles, and range-based k NN queries in obstructed space. At last, we conduct a comprehensive performance evaluation using both real and synthetic datasets to validate our ideas and the proposed algorithms. The experimental result shows that the RONN-OBA algorithm outperforms the two R-tree based algorithms and RONN-OA significantly.