23,063 research outputs found
Group Reverse Nearest Neighbor Search using Modified Skip Graph
The reverse nearest neighbor search is used for spatial queries. The reverse nearest neighbor search, the object in high dimensional space has a certain region where all objects inside the region will think of query object as their nearest neighbor. The existing methods for reverse nearest neighbor search are limited to the single query point, which is inefficient for the high dimensional spatial databases etc. Therefore, in this paper we proposed a group reverse nearest neighbor search which can find multiple query objects in a specific region. In this paper we proposed method for group reverse nearest neighbor queries using modified skip graph
Continuous Nearest Neighbor Queries over Sliding Windows
Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.
Nearest Neighbor for Inter-Building Environment
Nearest neighbor is one of the most common spatial database queries. The query has been implemented in outdoor space to find the nearest object of interests from query location. While nearest neighbor queries are commonly used in outdoor, it is hard to be implemented in indoor space due to lack of geo-positioning system that can be used in indoor space. Moreover, the network structure and the objects of interest types in indoor environment make nearest neighbor query difficult to implemented straight away in indoor environment. This paper adapts nearest neighbor in indoor space for inter-building environment without geo-positioning and discover the shortest path to nearest object. Our experiment show that nearest neighbor could be adapted in indoor spaces by using road network in indoor and implement routing algorithm for routing to the nearest object.
Keyword : Nearest neighbor, indoor space
Approximate Nearest Neighbor Search for Low Dimensional Queries
We study the Approximate Nearest Neighbor problem for metric spaces where the
query points are constrained to lie on a subspace of low doubling dimension,
while the data is high-dimensional. We show that this problem can be solved
efficiently despite the high dimensionality of the data.Comment: 25 page
Chromatic k-Nearest Neighbor Queries
Let be a set of colored points. We develop efficient data structures
that store and can answer chromatic -nearest neighbor (-NN) queries.
Such a query consists of a query point and a number , and asks for the
color that appears most frequently among the points in closest to .
Answering such queries efficiently is the key to obtain fast -NN
classifiers. Our main aim is to obtain query times that are independent of
while using near-linear space.
We show that this is possible using a combination of two data structures. The
first data structure allow us to compute a region containing exactly the
-nearest neighbors of a query point , and the second data structure can
then report the most frequent color in such a region. This leads to linear
space data structures with query times of for points in
, and with query times varying between
and , depending on the distance measure used, for
points in . Since these query times are still fairly large we
also consider approximations. If we are allowed to report a color that appears
at least times, where is the frequency of the most
frequent color, we obtain a query time of in and expected query
times ranging between and
in using near-linear
space (ignoring polylogarithmic factors).Comment: 37 pages, 9 figure
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