Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e

In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed

Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed

In this paper, we discuss static and dynamic data structures for answering reverse nearest neighbor queries in two dimensions.

We present a method for performing fault localization using similar program spectra. Our method assumes the existence of a faulty run and a larger number of correct runs. It then selects according to a distance criterion the correct run that most resembles the faulty run, compares the spectra corresponding to these two runs, and produces a report of "suspicious" parts of the program. Our method is widely applicable because it does not require any knowledge of the program input and no more information from the user than a classification of the runs as either "correct" or "faulty". To experimentally validate the viability of the method, we implemented it in a tool, WHITHER using basic block profiling spectra. We experimented with two different similarity measures and the Siemens suite of 132 programs with injected bugs. To measure the success of the tool, we developed a generic method for establishing the quality of a report. The method is based on the way an "ideal user" would navigate the program using the report to save effort during debugging. The best results we obtained were, on average, above 50%, meaning that our ideal user would avoid looking at half of the program.

A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper-)rectangles and propose efficient in

Nearest neighbor queries on R-trees use a number of pruning techniques to improve the search. We examine three common 1-nearest neighbor pruning strategies and generalize them to #-nearest neighbors. This generalization clears up a number of prior misconceptions. Specifically, we show

Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q1, q2 and q3 that want to find a meeting point (e.g., a restaurant); the corresponding query

neighbor queries

Abstract. We introduce the visible k nearest neighbor (VkNN) query, which finds the k nearest objects that are visible to a query point. We also propose an algorithm to efficiently process the VkNN query. We compute the visible neighbors incrementally as we enlarge the search space. Our algorithm