775 research outputs found
CSD: Discriminance with Conic Section for Improving Reverse k Nearest Neighbors Queries
The reverse nearest neighbor (RNN) query finds all points that have
the query point as one of their nearest neighbors (NN), where the NN
query finds the closest points to its query point. Based on the
characteristics of conic section, we propose a discriminance, named CSD (Conic
Section Discriminance), to determine points whether belong to the RNN set
without issuing any queries with non-constant computational complexity. By
using CSD, we also implement an efficient RNN algorithm CSD-RNN with a
computational complexity at . The comparative
experiments are conducted between CSD-RNN and other two state-of-the-art
RkNN algorithms, SLICE and VR-RNN. The experimental results indicate that
the efficiency of CSD-RNN is significantly higher than its competitors
K-nearest neighbor search for fuzzy objects
The K-Nearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. Existing research on fuzzy objects mainly focuses on modelling basic fuzzy object types and operations, leaving the processing of more advanced queries such as kNN query untouched. In this paper, we propose two new kinds of kNN queries for fuzzy objects, Ad-hoc kNN query (AKNN) and Range kNN query (RKNN), to find the k nearest objects qualifying at a probability threshold or within a probability range. For efficient AKNN query processing, we optimize the basic best-first search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To improve the performance of RKNN search, effective pruning rules are developed to significantly reduce the search space and further speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments using both synthetic and real datasets
Distance Range Queries in SpatialHadoop
Efficient processing of Distance Range Queries (DRQs) is of great importance in spatial databases due to the wide area of applications. This type of spatial query is characterized by a distance range over one or two datasets. The most representative and known DRQs are the ε Distance Range Query (εDRQ) and the ε Distance Range Join Query (εDRJQ). Given the increasing volume of spatial data, it is difficult to perform a DRQ on a centralized machine efficiently. Moreover, the εDRJQ is an expensive spatial operation, since it can be considered a combination of the εDR and the spatial join queries. For this reason, this paper addresses the problem of computing DRQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes new algorithms in SpatialHadoop to perform efficient parallel DRQs on large-scale spatial datasets. We have evaluated the performance of the proposed algorithms in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal
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