Processing Approximate KNN Query Based on Data Source Selection
2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)(2021)
摘要
A KNN query over a relation is to find its
$K$
nearest neighbors/tuples from a dataset/relation according to a distance function. In this paper, we discuss approximate KNN query processing based on the selection of many data sources with various dimensions. We propose algorithms to construct a UBR- Tree and a Centroid Base for selecting related data sources and retrieving
$K$
NN tuples. For a
$K$
NN query
$Q$
, (1) the related data sources are selected by using the Centroid Base, (2) these data sources are sorted according to their representative tuple in the Centroid Base, (3) local
$K$
NN tuples in the related data sources are retrieved, and (4) a heap structure is used to merge the local
$K$
NN tuples to form global
$K$
NN tuples of
$Q$
. Extensive experiments over low-dimensional and high-dimensional datasets are conducted to demonstrate the performances of our proposed approaches.
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关键词
data source selection,approximate KNN query processing,centroid base,global KNN tuples,distance function,UBR tree,local KNN tuples,k-nearest neighbors
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