A multi-resolution surface distance model for k-NN query processing

Abstract

A spatial k-NN query returns k nearest points in a point dataset to a given query point. To measure the distance between two points, most of the literature focuses on the Euclidean distance or the network distance. For many applications, such as wildlife movement, it is necessary to consider the surface distance, which is computed from the shortest path along a terrain surface. In this paper, we investigate the problem of efficient surface k-NN (sk-NN ) query processing. This is an important yet highly challenging problem because the underlying environment data can be very large and the computational cost of finding the shortest path on a surface can be very high. To minimize the amount of surface data to be used and the cost of surface distance computation, a multiresolution surface distance model is proposed in this paper to take advantage of monotonic distance changes when the distances are computed at different resolution levels. Based on this innovative model, sk-NN queries can be processed efficiently by accessing and processing surface data at a justenough resolution level within a just-enough search region. Our extensive performance evaluations using real world datasets confirm the efficiency of our proposed model.

Indexing land surface for efficient kNN query

“…The only previous approach to the skNN problem is discussed in [8], with an extended version in [13]. Deng et al propose a distance range ranking method for the skNN query on the multiresolution terrain model.…”

“…In this so called surface k Nearest Neighbor queries, the objects can only move on the terrain surfaces and the actual distance depends on the shortest surface path between two points. Besides the applications mentioned in [8] [13], skNN query is also useful for tourism applications, evacuation planning and military operations. Furthermore, different variations of skNN such as the continuous skNN query where sites (e.g., vehicles) are moving and the visible skNN query enable many other interesting applications.…”

“…Unlike other types of kNN queries, there are few studies on skNN queries [8] [13], largely due to the following three challenges:…”

“…In [13], it is reported that this operation will take tens of minutes on a modern PC machine. Note that the surface distance computation is fundamental and frequent in the skNN query.…”

“…We are only aware of one study addressing skNN [8] [13], in which the authors employed a filter and refinement strategy to process skNN queries. The idea is to answer skNN queries by ranking the sites based on the range of their upper and lower bound distances to the query point.…”