• Journal of KIISE:Databases
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• v.31 no.1
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• pp.30-38
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• 2004

In many scientific and commercial applications such as Earth Observation System (EOSDIS) and mobile Phone services tracking a large number of clients, it is a daunting task to archive and index ever increasing volume of complex data that are continuously added to databases. To efficiently manage multidimensional data in scientific and data warehousing environments, R-tree based index structures have been widely used. In this paper, we propose a scalable technique called seeded clustering that allows us to maintain R-tree indexes by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion as well as the results from our extensive experimental study. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods in terms of insertion cost and the quality of target R-trees measured by their query performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.129-132
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• 2004

공간 색인구조는 공간 데이터를 효율적으로 관리하기 위한 도구로써, GIS와 같은 공간 데이터베이스의 성능을 결정하는 중요한 요소라 하겠다. 대부분의 응용분야에서 공간 데이터베이스는 보조기억장치에 저장된 방대한 양의 공간데이터 처리를 요구하므로 디스크 접근의 수를 줄이는 것이 전체 데이터베이스의 성능을 향상시키는데 중요한 요소이다. 이 논문에서는 SMR-tree라는 공간색인구조의 여러 응용분야에서 활용 가능성을 기존의 색인구조들과의 비교를 통해 확인한다. SMR-tree는 R-tree 계열의 구조로써 기존의 R-tree계열의 구조들과 동일한 노드의 형태를 가지고 있으나, 여러 개의 data space를 사용하여 data object를 배분함으로써 $R^{+}-tree$의 말단노드 내에 존재하는 잉여공간을 제거하면서 R-tree의 단점인 색인노드들 사이에 중첩을 허용치 않는다. SMR-tree의 성능은 여러 종류의 테스트 데이터(VLSI layout data, Tiger/Line file data)를 사용하여 R-tree, $R^{+}-tree,\;R^{\ast}-tree$와 비교된다. SMR-tree는 높은 공간 활용도와 다른 색인구조에 비해 빠른 질의 성능을 보임으로써 GIS와 같은 공간 데이터베이스를 위한 효율적인 색인구조로 사용이 될 것으로 기대된다.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.65-75
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• 2005

Moving-objects databases should efficiently support database queries that refer to the trajectories and positions of continuously moving objects. To improve the performance of these queries. an efficient indexing scheme for continuously moving objects is required. To my knowledge, range queries on current positions cannot be handled by the 3D R-tree and the TB-tree. In order to handle range queries on current and past positions. I modified the original 3D R-tree to keep the now tags. Most of spatio-temporal index structures suffer from the fact that they cannot efficiently process range queries past positions of moving objects. To address this issue. we propose an access method, called the Tagged Adaptive 3DR-tree (or just TA3DR-tree), which is based on the original 3D R-tree method. The results of our extensive experiments show that the Tagged Adaptive 3DR-tree outperforms the original 3D R-tree and the TB-tree typically by a big margin.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.171-173
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• 1999

공간 데이터베이스에서 사용되는 데이터는 그 양이 방대하고 복잡하여 이를 효율적으로 저장, 관리하는 색인이 필요하다. 여러 공간 색인 방법들 중에서 R-tree는 삽입과 삭제가 빈번히 발생하는 동적인 환경에서 효율적인 질의 성능을 보이는 것으로 알려져 있다. R-tree는 삽입되는 데이터의 순서에 따라 트리의 구조가 달라질 수 있는데, 주어진 데이터가 수정이 자주 발생하지 않는다며 데이터 입력 순서를 결정하여 질의 성능이 가장 좋은 트리를 구성할 수 있다. 본 논문에서는 데이터가 자주 수정되지 않는 환경에서 노드간의 중첩을 가장 최소화 할 수 있는 데이터 입력 순서를 결정하기 위해 클러스터링을 이용한 새로운 방법인 CSR-tree를 제안하고자 한다. CSR-tree는 일반 R-tree와 hilbert packed R-tree 방법보다 향상된 질의 성능을 보인다.

• Spatial Information Research
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• v.19 no.2
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• pp.47-56
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• 2011

Typical tree-based spatial index structures are divided into a data-partitioning index structure such as R-Tree and a space-partitioning index structure such as KD-Tree. In recent years, researches on hybrid index structures combining advantages of these index structures have been performed extensively. However, because the split boundary extension of the node to which a new spatial object is inserted may extend split boundaries of other neighbor nodes in existing researches, overlaps between nodes are increased and the query processing cost is raised. In this paper, we propose a hybrid index structure, called SQR-Tree that can support efficient processing of spatial queries to solve these problems. SQR-Tree is a combination of SQ-Tree(Spatial Quad- Tree) which is an extended Quad-Tree to process non-size spatial objects and R-Tree which actually stores spatial objects associated with each leaf node of SQ-Tree. Because each SQR-Tree node has an MBR containing sub-nodes, the split boundary of a node will be extended independently and overlaps between nodes can be reduced. In addition, a spatial object is inserted into R-Tree in each split data space and SQ-Tree is used to identify each split data space. Since only R-Trees of SQR-Tree in the query area are accessed to process a spatial query, query processing cost can be reduced. Finally, we proved superiority of SQR-Tree through experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.878-881
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• 2008

To index the object's trajectory is an important aspect in moving object database management. This paper implements an optimizing index structure named Rend 3D R-tree based on 3D R-Tree. This paper demonstrates the time period update method to reconstruct the MBR for the moving objects in order to decrease the dead space that is produced in the closed time dimension of the 3D R-tree, then a rend method is introduced for indexing both current data and history data. The result of experiments illustrates that given methods outperforms 3D R-Tree and LUR tree in query processes.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.111-123
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• 2005

Many indexing methods were proposed so that process moving object efficiently. Among them, indexing methods like the 3D R-tree treat temporal and spatial domain as the same. Actually, however. both domain had better process separately because of difference in character and unit. Especially in this paper we deal with limited region such as indoor environment since spatial domain is limited but temporal domain is grown. In this paper we present a novel indexing structure, namely STS-tree(Separation of Time and Space tree). based on limited region. STS-tree is a hybrid tree structure which consists of R-tree and one-dimensional TB-tree. The R-tree component indexes static object and spatial information such as topography of the space. The TB-tree component indexes moving object and temporal information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.39-46
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• 2011

The present study introduces a comparison between 3D R-tree and octree which are noticeable candidates to index large point clouds gathered from a 3D terrestrial laser scanner. A query method, which is to find neighboring points within given distances, was devised for the comparison, and time lapses for the query along with memory usages were checked. From tests conducted on point clouds scanned from a building and a stone pagoda, it was shown that octree has the advantage of fast generation and query while 3D R-tree is more memory-efficient. Both index and leaf capacity were revealed to be ruling factors to get the best performance of 3D R-tree, while the number of level was of oetree.