• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.63-74
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• 2005

The telematics device, which is a typical embedded system on the transportation or vehicle, requires the embedded spatial DBMS based on RTOS (Real Time Operating System) for processing the huge spatial data in real time. This spatial DBMS can be developed very easily by SQL3 functions of the ORDBMS such as UDT (user-defined type) and UDF (user-defined function). However, developing index suitable for the embedded spatial DBMS is very difficult. This is due to the fact that there is no built-in SQL3 functions to construct spatial indexes. In this study, we compare and analyze both Generalized Search Tree and Relational Indexing methods which are suggested as common ways of developing User-Defined Indexes nowadays. Two implementations of R-Tree based on each method were done and region query performance test results were evaluated for suggesting a suitable indexing method of an embedded spatial DBMS, especially for telematics devices.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.10-12
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• 2004

Nowadays, with the rapid emergence of XML as a standard for data exchange over the Internet had led to considerable interest In the problem of data management requirements such as the need to store and query XML documents in which the location path languages Xpath is of particular important for XML application since it is a core component of many XML processing standards such as XSLT or XQuery, This parer gives a brief overview about method and design by applying a new spatial-based indexing method namely Xp-free that used for supporting Xpath. Spatial indexing technique has been proved its capacity on searching in large databases. Based on accelerating a node using planar as combined with the numbering schema, we devise efficiently derivative algorithms, which are simple, but useful. Besides that, it also allows to trace all Its relative nodes of context node In a manner supporting queries natural to the types especially Xpath queries with predicates.

• Spatial Information Research
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• v.5 no.1
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• pp.1-10
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• 1997

In this paper, we classify existing spatial indexing schemes for spatial objects in geographic database systems into seven classes. Also, we propose a new spatial indexing scheme called MAX(Multi-Attribute indeXing scheme). The search, insert, delete algorithms for the proposed indexmg scheme are described in detail. It is expected that the performance of the proposed indexing scheme is better than the existing indexing schemes under the some conditions. The proposed indexing scheme, MAX, can be easily implemented on existing built-in B-trees in most storage managers in the sense tha.t the structure of MAX is like that of B-tree.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.317-324
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• 2006

In this paper, we propose a new indexing technique to solve various queries which have a strong good point not only database indexing schema take advantage of converting from semi-structured data to structured data but also performance is more faster than before. We represent structure information of XML document between nodes of tree that additional numbering information which can be bit-stream without modified structure of XML tree. And, We add in indexing schema searching incidental structure information in the process. In Querying schema, we recover ancestor nodes through give information of node using indexing schema in complete path query expression as well as relative path query expression. Therefore, it takes advantage of making derivative query expression with given query. In this process, we recognize that indexing and querying schema can get searched result set faster and more accurate. Because response time is become shorter by bit operating, when query occur and it just needs information of record set earch node in database.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.19-29
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• 2004

Recently, studies have been performed to improve the cache performance of the R-Tree in main memory. A general mothed to improve the cache performance of the R-Tree is to reduce size of an entry so that a node can store more entries and fanout of it can increase. However, this method generally requites additional process to reduce information of entries and do not support incremental updates. In addition, the cache miss always occurs on moving between a parent node and a child node. To solve these problems efficiently, this paper proposes and evaluates the PR-Tree that is an extended R-Tree indexing method using prefetching in main memory. The PR-Tree can produce a wider node to optimize prefetching without additional modifications on the R-Tree. Moreover, the PR-Tree reduces cache miss rates that occur in moving between a parent node and a child node. In our simulation, the search performance, the update performance, and the node split performance of the PR-Tree improve up to 38%. 30%, and 67% respectively, compared with the original R-Tree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.523-531
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• 2007

In this paper, we are going to develop a newly designed indexing scheme which is compatible to manage the moving objects and propose a cost model of the scheme. We propose a dynamic hashing index that insertion/delete costs are low. The dynamic hashing structure is that apply dynamic hashing techniques to combine a hash and a tree to a spatial index. We analyzed the dynamic index structure and the cost model by the frequent position update of moving objects and verified through a performance assessment experiment. The results of our extensive experiments show that the newly proposed indexing schemes(Dynamic Hashing Index) are much more efficient than the traditional the fixed grid and R-tree.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.15-22
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• 2004

Map services for cellular phone have problem for implementation, which are the limitation of a screen size. To effectively represent map data on screen of celluar phone, it need a process which translate a detailed map data into less detailed data using map generalization, and it should manipulate zoom in out quickly by leveling the generalized data. However, current spatial indexing methods supporting map generalization do not support all map generalization operations. In this paper, We propose a leveled spatial indexing method, LMG-tree, supporting map generalization and presents the results of performance evaluation.

• ETRI Journal
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• v.33 no.5
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• pp.741-751
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• 2011

This paper focuses on online speaker tracking for telephone conversations and broadcast news. Since the online applicability imposes some limitations on the tracking strategy, such as data insufficiency, a reliable approach should be applied to compensate for this shortage. In this framework, a set of reference speaker models are used as side information to facilitate online tracking. To improve the indexing accuracy, adaptation approaches in eigenvoice decomposition space are proposed in this paper. We believe that the eigenvoice adaptation techniques would help to embed the speaker space in the models and hence enrich the generality of the selected speaker models. Also, an index structure of the reference models is proposed to speed up the search in the model space. The proposed framework is evaluated on 2002 Rich Transcription Broadcast News and Conversational Telephone Speech corpus as well as a synthetic dataset. The indexing errors of the proposed framework on telephone conversations, broadcast news, and synthetic dataset are 8.77%, 9.36%, and 12.4%, respectively. Using the index tree structure approach, the run time of the proposed framework is improved by 22%.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.