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SQMR-tree: An Efficient Hybrid Index Structure for Large Spatial Data (SQMR-tree: 대용량 공간 데이타를 위한 효율적인 하이브리드 인덱스 구조)

  • Shin, In-Su;Kim, Joung-Joon;Kang, Hong-Koo;Han, Ki-Joon
    • Spatial Information Research
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    • v.19 no.4
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    • pp.45-54
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    • 2011
  • In this paper, we propose a hybrid index structure, called the SQMR-tree(Spatial Quad MR-tree) that can process spatial data efficiently by combining advantages of the MR-tree and the SQR-tree. The MR-tree is an extended R-tree using a mapping tree to access directly to leaf nodes of the R-tree and the SQR-tree is a combination of the SQ-tree(Spatial Quad-tree) which is an extended Quad-tree to process spatial objects with non-zero area and the R-tree which actually stores spatial objects and are associated with each leaf node of the SQ-tree. The SQMR-tree consists of the SQR-tree as the base structure and the mapping trees associated with each R-tree of the SQR-tree. Therefore, because spatial objects are distributedly inserted into several R-trees and only R-trees intersected with the query area are accessed to process spatial queries like the SQR-tree, the query processing cost of the SQMR-tree can be reduced. Moreover, the search performance of the SQMR-tree is improved by using the mapping trees to access directly to leaf nodes of the R-tree without tree traversal like the MR-tree. Finally, we proved superiority of the SQMR-tree through experiments.

An Efficient Hybrid Spatial Index Structure based on the R-tree (R-tree 기반의 효율적인 하이브리드 공간 인덱스 구조)

  • Kang, Hong-Koo;Kim, Joung-Joon;Han, Ki-Joon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2009.11a
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    • pp.771-772
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    • 2009
  • 최근 대표적인 공간 인덱스 구조인 R-tree를 기반으로 KD-tree나 Quad-tree와 같은 공간 분할 특성을 이용하여 인덱싱 성능을 향상시키기 위한 연구가 활발하다. 본 논문에서는 기존에 제시된 R-tree 기반 인덱스 구조인 SQR-tree와 PMR-tree의 특성을 결합하여 대용량 공간 데이타를 보다 효율적으로 처리하는 인덱스 구조인 MSQR-tree(Mapping-based SQR-tree)를 제시한다. SQR-tree는 Quad-tree를 확장한 SQ-tree와 각 SQ-tree 리프 노드마다 실제로 공간 객체를 저장하는 R-tree가 연계되어 있는 인덱스 구조이고, PMR-tree는 R-tree에 R-tree 리프 노드를 직접 접근할 수 있는 매핑 트리를 적용한 인덱스 구조이다. 본 논문에서 제시하는 MSQR-tree는 SQR-tree를 기본 구조로 가지고 R-tree마다 매핑 트리가 적용된 구조를 갖는다. 따라서, MSQR-tree에서는 SQR-tree와 같이 질의가 여러 R-tree에서 분산 처리되고, PMR-tree와 같이 매핑 트리를 통해 R-tree 리프 노드를 빠르게 접근할 수 있다. 마지막으로 성능 실험을 통해 MSQR-tree의 우수성을 입증하였다.

An Index Data Structure for String Search in External Memory (외부 메모리에서 문자열을 효율적으로 탐색하기 위한 인덱스 자료 구조)

  • Na, Joong-Chae;Park, Kun-Soo
    • Journal of KIISE:Computer Systems and Theory
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    • v.32 no.11_12
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    • pp.598-607
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    • 2005
  • We propose a new external-memory index data structure, the Suffix B-tree. The Suffix B-tree is a B-tree in which the key is a string like the String B-tree. While the node in the String B-tree is implemented with a Patricia trio, the node in the Suffix B-tree is implemented with an array. So the Suffix B-tree is simpler and easier to be Implemented than the String B-tree. Nevertheless, the branching algorithm of the Suffix B-tree is as efficient as that of the String B-tree. Consequently, the Suffix B-tree takes the same worst-case disk accesses as the String B-tree to solve the string matching problem, which is fundamental and important in the area of string algorithms.

A study on the Art Style of Sacred Tree - Focusing on the Assyrian Style Tree Pattern - (성수문(聖樹文)에 대(對)한 연구(硏究) - 앗시리아식(式) 수목중심문양(樹木中心文樣)을 중심(中心)으로 -)

  • Kim, Moon-Ja
    • Journal of Fashion Business
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    • v.5 no.3
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    • pp.63-71
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    • 2001
  • The symbol of the sacred tree represents the world tree. They were influenced by Tree worship in Northern mounted nomadic groups, and the first is quite obvious, that the sacred tree is a palm tree. The Assyrian sacred tree possesses characteristics, making the tree iconographic and rather artificial. The tree typically has a thin trunk arranged in two or three tiers. Each tier is separated by horizontal plates or bands. The top of the tree is crowned with a palmette form. Wavy streamers emanate from the tree abd terminate in palmettes in a criss cross fashion. The central trunk is topped with a palmette and surrounded the trunk with palmettes emphasizing a link to date trees. The number of branches on the tree is limited, and there are usually seven, fifteen or thirty branches. The connection of these numbers with those of the week, and of the lunar wax and wane is so obvious. The branches on the tree may have indeed represented a calender of some sort. Mainly based on the excavated tomb articles of the three kingdoms and referred to Chinese and Japanese ones, Sacred Tree pattern showed that was lightly influenced by the times and area, but was slowly changed and developed to different types through those each ages generally. The Sacred Tree type was three part in according to the wavy streamers emanate from the tree abd, Sacred tree type I, Sacred tree type II, Sacred tree type III[the Mountain(; 山)-typed piled up Sacred Tree].

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MR-Tree: A Mapping-based R-Tree for Efficient Spatial Searching (Mr-Tree: 효율적인 공간 검색을 위한 매핑 기반 R-Tree)

  • Kang, Hong-Koo;Shin, In-Su;Kim, Joung-Joon;Han, Ki-Joon
    • Spatial Information Research
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    • v.18 no.4
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    • pp.109-120
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    • 2010
  • Recently, due to rapid increasement of spatial data collected from various geosensors in u-GIS environments, the importance of spatial index for efficient search of large spatial data is rising gradually. Especially, researches based R-Tree to improve search performance of spatial data have been actively performed. These previous researches focus on reducing overlaps between nodes or the height of the R -Tree. However, these can not solve an unnecessary node access problem efficiently occurred in tree traversal. In this paper, we propose a MR-Tree(Mapping-based R-Tree) to solve this problem and to support efficient search of large spatial data. The MR-Tree can improve search performance by using a mapping tree for direct access to leaf nodes of the R-Tree without tree traversal. The mapping tree is composed with MBRs and pointers of R-Tree leaf nodes associating each partition which is made by splitting data area repeatedly along dimensions. Especially, the MR-Tree can be adopted in various variations of the R-Tree easily without a modification of the R-Tree structure. In addition, because the mapping tree is constructed in main memory, search time can be greatly reduced. Finally, we proved superiority of MR-Tree performance through experiments.

SQR-Tree : A Hybrid Index Structure for Efficient Spatial Query Processing (SQR-Tree : 효율적인 공간 질의 처리를 위한 하이브리드 인덱스 구조)

  • Kang, Hong-Koo;Shin, In-Su;Kim, Joung-Joon;Han, Ki-Joon
    • 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.

A Cost Model for the Performance Prediction of the TPR-tree (TPR-tree의 성능 예측을 위한 비용 모델)

  • 최용진;정진완
    • Journal of KIISE:Databases
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    • v.31 no.3
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    • pp.252-260
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    • 2004
  • Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.

IRFP-tree: Intersection Rule Based FP-tree (IRFP-tree(Intersection Rule Based FP-tree): 메모리 효율성을 향상시키기 위해 교집합 규칙 기반의 패러다임을 적용한 FP-tree)

  • Lee, Jung-Hun
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.3
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    • pp.155-164
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    • 2016
  • For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

Ensemble Gene Selection Method Based on Multiple Tree Models

  • Mingzhu Lou
    • Journal of Information Processing Systems
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    • v.19 no.5
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    • pp.652-662
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    • 2023
  • Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

An Extended R-Tree Indexing Method using Prefetching in Main Memory (메인 메모리에서 선반입을 사용한 확장된 R-Tree 색인 기법)

  • Kang, Hong-Koo;Kim, Dong-O;Hong, Dong-Sook;Han, Ki-Joon
    • 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.

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