원문정보
초록
영어
Nearest Neighbor (NN) search has been widely used in spatial databases and multimedia
databases. Incremental NN (INN) search algorithm is regarded as the optimal NN search
because of the minimum number of node accesses and it can be used no matter whether the
number of objects to be retrieved is fixed or not in advance. This paper presents an analytical
model for estimating performance of the INN search algorithm. For the first time, our model
takes m (the number of neighbor objects reported finally), n (the cardinality of database) and
d (the dimensionality) as parameters, focusing on the number of node accesses (not only the
number of accessed leaf nodes) and the length of the priority queue. Using our model,
dimensionality curse is mathematically revealed for an arbitrary number of NN objects
retrieved. In our model, (1) for the first time, the two key factors of dm (the distance from the
m-th NN object to the query point) and σ
h (the side length of each node) are estimated using
their upper bounds and their lower bounds, which is helpful to effectiveness of our model,
especially in high-dimensional spaces; (2) for the first time, the possible difference of fanouts
among the leaf nodes, the root node and the others is taken into account. The theoretical
analysis is verified by experiments.
목차
1. Introduction
2 Related Works
2.1. R-trees
2.2. INN Search on R-trees
2.3. Performance Analysis of the INN Search Algorithm
3. Performance Estimation of the INN Search Algorithm
3.1. Propositions
3.2 Expected Distance from the Query Point to the m-th NN Object (i.e., dm)
3.3 Expected length of the Side of Node MBR
3.4 Expected Number of Node Accesses
3.5 Expected Length of the Priority Queue
3.6 Discussion of fr, fi, fl and H
4. Experimental Evaluation
4.1 Evaluation with Different Dimensionalities
4.2 Evaluation with Different m
4.3 Evaluation with Different Cardinalities of Databases
5. Conclusion
6. Reference