• Title/Summary/Keyword: Vector Approximation Tree

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Performance Enhancement of a DVA-tree by the Independent Vector Approximation (독립적인 벡터 근사에 의한 분산 벡터 근사 트리의 성능 강화)

  • Choi, Hyun-Hwa;Lee, Kyu-Chul
    • The KIPS Transactions:PartD
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    • v.19D no.2
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    • pp.151-160
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    • 2012
  • Most of the distributed high-dimensional indexing structures provide a reasonable search performance especially when the dataset is uniformly distributed. However, in case when the dataset is clustered or skewed, the search performances gradually degrade as compared with the uniformly distributed dataset. We propose a method of improving the k-nearest neighbor search performance for the distributed vector approximation-tree based on the strongly clustered or skewed dataset. The basic idea is to compute volumes of the leaf nodes on the top-tree of a distributed vector approximation-tree and to assign different number of bits to them in order to assure an identification performance of vector approximation. In other words, it can be done by assigning more bits to the high-density clusters. We conducted experiments to compare the search performance with the distributed hybrid spill-tree and distributed vector approximation-tree by using the synthetic and real data sets. The experimental results show that our proposed scheme provides consistent results with significant performance improvements of the distributed vector approximation-tree for strongly clustered or skewed datasets.

Multi-Dimensional Vector Approximation Tree with Dynamic Bit Allocation (동적 비트 할당을 통한 다차원 벡터 근사 트리)

  • 복경수;허정필;유재수
    • The Journal of the Korea Contents Association
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    • v.4 no.3
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    • pp.81-90
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    • 2004
  • Recently, It has been increased to use a multi-dimensional data in various applications with a rapid growth of the computing environment. In this paper, we propose the vector approximate tree for content-based retrieval of multi-dimensional data. The proposed index structure reduces the depth of tree by storing the many region information in a node because of representing region information using space partition based method and vector approximation method. Also it efficiently handles 'dimensionality curse' that causes a problem of multi-dimensional index structure by assigning the multi-dimensional data space to dynamic bit. And it provides the more correct regions by representing the child region information as the parent region information relatively. We show that our index structure outperforms the existing index structure by various experimental evaluations.

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VA-Tree : An Efficient Multi-Dimensional Index Structure for Large Data Set (VA-Tree : 대용량 데이터를 위한 효율적인 다차원 색인구조)

  • 송석일;이석희;조기형;유재수
    • Journal of Korea Multimedia Society
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    • v.6 no.5
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    • pp.753-768
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    • 2003
  • In this paper, we propose a multi-dimensional index structure, tailed a VA(Vector Approximate)-tree that is constructed with vector approximates of multi-dimensional feature vectors. To save storage space for index structures, the VA-tree employs vector approximation concepts of VA-file that presents feature vectors with much smaller number of bits than original value. Since the VA-tree is a tree structure, it does not suffer from performance degradation owing to the increase of data. Also, even though the VA-tree is MBR(Minimum Bounding Region) based tree structure like a R-tree, its split algorithm never allows overlap between MBRs. We show through various experiments that our proposed VA-tree is a suitable index structure for large amount of multi-dimensional data.

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An Efficient Multi-Dimensional Index Structure for Large Data Set (대용량 데이터를 위한 효율적인 다차원 색인구조)

  • Lee, ByoungYup;Yoo, Jae-Soo
    • Journal of the Korean Association of Geographic Information Studies
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    • v.5 no.2
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    • pp.54-68
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    • 2002
  • In this paper, We propose a multi-dimensional index structure, called a VA (vector approximate) -tree that constructs a tree with vector approximates of multi-dimensional feature vectors. To save storage space for index structures, the VA-tree employs vector approximation concepts of VA-file that presents feature vectors with much smaller number of bits than original value. Since the VA-tree is a tree structure, it does not suffer from performance degradation owing to the increase of data. Also, even though the VA-tree is MBR Minimum Bounding Region) based tree structure like a R-tree, its split algorithm never allows overlap between MBRs. We show through various experiments that our proposed VA-tree is the efficient index structure for large amount of multi-dimensional data.

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A Distributed High Dimensional Indexing Structure for Content-based Retrieval of Large Scale Data (대용량 데이터의 내용 기반 검색을 위한 분산 고차원 색인 구조)

  • Cho, Hyun-Hwa;Lee, Mi-Young;Kim, Young-Chang;Chang, Jae-Woo;Lee, Kyu-Chul
    • Journal of KIISE:Databases
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    • v.37 no.5
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    • pp.228-237
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    • 2010
  • 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 data. To support these requirements, we propose a distributed high-dimensional indexing structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and 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 the performance of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method contributes to significant performance advantages over existing index structures on difference kinds of datasets.

GC-Tree: A Hierarchical Index Structure for Image Databases (GC-트리 : 이미지 데이타베이스를 위한 계층 색인 구조)

  • 차광호
    • Journal of KIISE:Databases
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    • v.31 no.1
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    • pp.13-22
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    • 2004
  • With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

An Index Structure based on Space Partitions and Adaptive Bit Allocations for Multi-Dimensional Data (다차원 데이타를 위한 공간 분할 및 적응적 비트 할당 기반 색인 구조)

  • Bok, Kyoung-Soo;Kim, Eun-Jae;Yoo, Jae-Soo
    • Journal of KIISE:Databases
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    • v.32 no.5
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    • pp.509-525
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    • 2005
  • In this paper, we propose the index structure based on a vector approximation for efficiently supporting the similarity search of multi-dimensional data. The proposed index structure splits a region with the space partition method and allocates to the split region dynamic bits according to the distribution of data. Therefore, the index structure splits a region to the unoverlapped regions and can reduce the depth of the tree by storing the much region information of child nodes in a internal node. Our index structure represents the child node more exactly and provide the efficient search by representing the region information of the child node relatively using the region information of the parent node. We show that our proposed index structure is better than the existing index structure in various experiments. Experimental results show that our proposed index structure achieves about $40\%$ performance improvements on search performance over the existing method.