Range-Based Nearest Neighbor Queries with Complex-Shaped Obstacles

Huaijie Zhu, Xiaochun Yang, Bin Wang, Wang-chien Lee

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Abstract

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 obstructed nearest-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-obstructed subspaces, 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.

Original languageEnglish (US)
Pages (from-to)963-977
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number5
DOIs
StatePublished - May 1 2018

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All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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