• 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.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.43-53
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• 2011

The many cities are changing in the city form where the person and nature are mixed. Namely, the government invests many expense in tree field of distance space for the change in the green city. In this paper, we design and implement a tree growth management system using PDA built in 13.56MHz RFID reader and CBD(Component Based Development) for ubiquitous computing environments. Our system provides history management to increase business efficiency for location coordinate of tree and history information of tree which using RFID, the RFlD tag is attaching the new tree and that is inputting GPS location information in PDA and provides tree information of tree by location coordinate to history management. Finally, we show from a performance analysis that our system achieves about 85% average tree read rate of RFID under test scenario environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.527-547
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• 2009

Moving object management is widely used in traffic, logistic and data mining applications in ubiquitous environments. It is required to analyze spatio-temporal data and trajectories for moving object management. In this paper, we proposed a novel index structure for spatio-temporal aggregation of trajectory in a constrained network, named aCN-RB-tree. It manages aggregation values of trajectories using a constraint network-based index and it also supports direction of trajectory. An aCN-RB-tree consists of an aR-tree in its center and an extended B-tree. In this structure, an aR-tree is similar to a Min/Max R-tree, which stores the child nodes' max aggregation value in the parent node. Also, the proposed index structure is based on a constrained network structure such as a FNR-tree, so that it can decrease the dead space of index nodes. Each leaf node of an aR-tree has an extended B-tree which can store timestamp-based aggregation values. As it considers the direction of trajectory, the extended B-tree has a structure with direction. So this kind of aCN-RB-tree index can support efficient search for trajectory and traffic zone. The aCN-RB-tree can find a moving object trajectory in a given time interval efficiently. It can support traffic management systems and mining systems in ubiquitous environments.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2001.11a
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• pp.207-208
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• 2001

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.332-335
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• 2008

In this paper, based on H-tree, which is proposed as the basic data cube structure for multi-dimensional data stream analysis, we have done some analysis. We find there are a lot of redundant nodes in H-tree, and the tree-build method can be improved for saving not only memory, but also time used for inserting tuples. Also, to facilitate more fast and large amount of data stream analysis, which is very important for stream research, H^*-tree is designed and developed. Our performance study compare the proposed H^*-tree and H-tree, identify that H^*-tree can save more memory and time during inserting data stream tuples.

• 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 information and communication convergence engineering
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• v.21 no.1
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• pp.54-61
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• 2023

The family tree is one of the key elements of humanities classics research and is very important for accurately understanding people or families. In this paper, we introduce a method for automatically generating a family tree using information on interpersonal relationships (IIPR) from the Korean Classics Database (KCDB) and visualize interpersonal searches within a family tree using data-driven document JavaScript (d3.js). To date, researchers of humanities classics have wasted considerable time manually drawing family trees to understand people's influence relationships. An automatic family tree builder analyzes a database that visually expresses the desired family tree. Because a family tree contains a large amount of data, we analyze the performance and bottlenecks according to the amount of data for visualization and propose an optimal way to construct a family tree. To this end, we create an n-ary tree with fake data, visualize it, and analyze its performance using simulation results.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.8-16
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• 2022

In this paper, we propose a method of reconstructing the file system to obtain digital forensics information from the APFS file system when meta information that can know the structure of the file system is deleted due to partial damage to the disk. This method is to reconstruct the tree structure of the file system by only retrieving the B-tree node where file/directory information is stored. This method is not a method of constructing nodes based on structural information such as Container Superblock (NXSB) and Volume Checkpoint Superblock (APSB), and B-tree root and leaf node information. The entire disk cluster is traversed to find scattered B-tree leaf nodes and to gather all the information in the file system to build information. It is a method of reconstructing a tree structure of a file/directory based on refined essential data by removing duplicate data. We demonstrate that the proposed method is valid through the results of applying the proposed method by generating numbers of user files and directories.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.111-117
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• 2007

We present an efficient decoding scheme for Huffman codes in which we use a properties of canonical prefix tree. After Huffman tree is converted to canonical Huffman tree, we represent Huffman tree with minimum information using rules associated with values of nodes in canonical tree. The proposed scheme can reduce memory to store Huffman tree information while maintains the same Processing time. The memory size in order to represent tree information is 2h + 2klogn which is less than those of previous methods. But the number of search is similar to previously proposed techniques.