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학술지 Distributed High Dimensional Indexing for K-NN Search
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저자
최현화, 이미영, 이규철
발행일
201211
출처
Journal of Supercomputing, v.62 no.3, pp.1362-1384
ISSN
0920-8542
출판사
Springer
DOI
https://dx.doi.org/10.1007/s11227-012-0800-z
협약과제
12VS1100, 유전체 분석용 슈퍼컴퓨팅 시스템 개발, 최완
초록
Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances, as well as to support index scalability for large-scale datasets. To support these requirements, we propose a distributed high-dimensional index structure based on cluster systems, called a Distributed Vector Approximationtree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and Vector Approximation files (VA-files). We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate performances of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method has significant performance advantages over existing index structures on different kinds of dataset. © Springer Science+Business Media, LLC 2012.
키워드
Approximate k-NN query, Cluster system, Distributed indexing, High-dimensional indexing
KSP 제안 키워드
An experimental study, Cluster system, Distributed indexing, High-Dimensional Data, High-dimensional index structure, Large-scale datasets, Level structure, NN query, Neighbor Search, Search Algorithm(GSA), Synthetic Datasets