Continuous obstructed nearest neighbor queries in spatial databases

Abstract

In this paper, we study a novel form of continuous nearest neighbor queries in the presence of obstacles, namely continuous obstructed nearest neighbor (CONN) search. It considers the impact of obstacles on the distance between objects, which is ignored by most of spatial queries. 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 of each point on q according to the obstructed distance, i.e., the shortest path between them without crossing any obstacle. We formulate 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 the CONN retrieval by performing a single query for the entire query segment, and only process the data points and obstacles relevant to the final result, via a novel concept of control points and an efficient quadraticbased split point computation algorithm. In addition, we extend our solution to handle the continuous obstructed k-nearest neighbor (COkNN) search, which finds the k (≥ 1) nearest neighbors to every point along q based on obstructed distances. A comprehensive experimental evaluation using both real and synthetic datasets has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms.

Querying visible points in large obstructed space

“…Thereby, it is crucial to find an efficient method of connecting query locations to obstacle vertices that are visible to them, which is the task of this paper. In the literature, interesting queries in obstacle space have been investigated such as obstructed nearest neighbors [8,31], visible nearest neighbors [10,23] and range queries [37]. However, the results are different from this paper.…”

“…An efficient algorithm is proposed in [31] to process data points and obstacles relevant to the query in an incremental way. Later, Gao et al [8] study the problem of finding continuous obstructed nearest neighbors by a query line segment instead of a point. A concept called control point is introduced to simplify the computation and comparison of the obstructed distance between two objects.…”

Processing moving k NN queries using influential neighbor sets

“…In recent years, other types of queries on moving objects have also been studied extensively. These include the range queries [1,26], the kNN queries with two predicates [2], the density queries [10], the intersection join queries [27,28], the obstructed NN queries [5,13], the visible NN queries [6], the weighted NN queries [14] and the destination prediction queries [24], etc. These studies have different problem settings from ours and their solutions are inapplicable.…”

On socio-spatial group query for location-based social networks

“…Relevant to our work, spatial queries for selecting a set of spatial points, aiming to minimize the total spatial distance, have been proposed for various scenarios [5,6,8,9]. However, in these works, the (social) connectivity among the spatial points is not considered.…”

“…On the other hand, for a line segment and a set of points, Continuous Nearest Neighbor Search [6] returns the nearest neighbor of each point on the line segment, while the Continuous Visible Nearest Neighbor Queries [8] extends Continuous Nearest Neighbor Search [6] by incorporating the obstacles in the problem design, which may affect the visibility or distance between two points and lead to different results. Meanwhile, Continuous Obstructed Nearest Neighbor Query [9] retrieves the nearest neighbor with regard to the obstructed distance, i.e., the shortest path without crossing any obstacle. Therefore, the above-mentioned queries focus only on the spatial dimension and thereby are not applicable to our scenario of LBSN applications.…”