Continuous nearest-neighbor search in the presence of obstacles

Author:

Gao Yunjun1,Zheng Baihua2,Chen Gang1,Chen Chun1,Li Qing3

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Singapore Management University, Singapore

3. City University of Hong Kong, Kowloon, Hong Kong

Abstract

Despite the ubiquity of physical obstacles (e.g., buildings, hills, and blindages, etc.) in the real world, most of spatial queries ignore the obstacles. In this article, we study a novel form of continuous nearest-neighbor queries in the presence of obstacles, namely continuous obstructed nearest-neighbor (CONN) search, which considers the impact of obstacles on the distance between objects. Given a data set P , an obstacle set O , and a query line segment q , in a two-dimensional space, a CONN query retrieves the nearest neighbor pP of each point p′ on q according to the obstructed distance, the shortest path between p and p without crossing any obstacle in O . We formalize CONN search, analyze its unique properties, and develop algorithms for exact CONN query-processing assuming that both P and O are indexed by conventional data-partitioning indices (e.g., R-trees). Our methods tackle CONN retrieval by performing a single query for the entire query line segment, and only process the data points and obstacles relevant to the final query result via a novel concept of control points and an efficient quadratic-based split point computation approach. Then, we extend our techniques to handle variations of CONN queries, including (1) continuous obstructed k nearest neighbor (CO k NN) search which, based on obstructed distances, finds the k (≥ 1) nearest neighbors (NNs) to every point along q ; and (2) trajectory obstructed k nearest-neighbor (TO k NN) search, which, according to obstructed distances, returns the k NNs for each point on an arbitrary trajectory (consisting of several consecutive line segments). Finally, we explore approximate CO k NN (ACO k NN) retrieval. Extensive experiments with both real and synthetic datasets demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.

Funder

Natural Science Foundation of Zhejiang Province

NBNSF

National Natural Science Foundation of China

Central Universities

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

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1. Processing Conflict-Aware $k$ Nearest Neighbor Queries in Euclidean Space;2024 IEEE International Conference on Big Data and Smart Computing (BigComp);2024-02-18

2. Closest Pairs Search Over Data Stream;Proceedings of the ACM on Management of Data;2023-11-13

3. Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment;Computer Modeling in Engineering & Sciences;2023

4. Processing Continuous k Nearest Neighbor Queries in Obstructed Space with Voronoi Diagrams;ACM Transactions on Spatial Algorithms and Systems;2021-02

5. On Efficiently Monitoring Continuous Aggregate k Nearest Neighbors in Road Networks;IEEE Transactions on Mobile Computing;2020-07-01

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