• 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.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.44-49
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• 2009

In recent years, with the development of portable terminals, various searching services on large data have been provided in portable terminals. In order to search large data, most applications for information retrieval use indexes such as B-trees or R-trees. However, only a small portion of the data set is accessed by users, and the access frequencies of each data are not uniform. The existing indexes such as B-trees or R-trees do not consider the properties of the skewed access patterns. And a cache stores the frequently accessed data for fast access in memory. But the size of memory used in the cache is restricted. In this paper, we propose a new index based on disk, called J-tree, which considers user's search patterns. The proposed index is a balanced tree which guarantees uniform searching time on all data. It also supports fast searching time on the frequently accessed data. Our experiments show the effectiveness of our proposed index under various settings.

• Journal of Korean Society of Forest Science
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• v.97 no.5
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• pp.501-507
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• 2008

The data of crown width with 4 directions, DBH, tree height, and coordinate for sample trees were collected from 30 permanent sample plots in secondary fore st of the Maoershan Experimental Forestry Farm, Northeast China. In this paper, the competition of individual trees in stand were discussed for secondary forest by using iterative Hegyi competition index and crown overlap index that represented the competitive and cooperative interactions among neighboring trees. Active competitors of subject tree in the competition zone were selected to calculate the iterative competition index. Using the results of crown classification based on the equal crown projection area, a new distance dependent competition index called crown overlap index (COI) was developed for secondary forest. The COI performed well in describing the crown competition rather than crown competition factor (CCF). The individual-based competition index discussed in this paper will provide more precise for developing individual tree growth models for secondary forest and it can also use to adjust the stand structure for spatial optimal management.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.150-162
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• 2004

Moving objects have characteristics that they change continuously their positions over time. The movement of moving objects should be stored on trajectories for processing past queries. Moving objects databases need to provide spatio-temporal index for handling moving objects queries like combined queries. Combined queries consist of a range query selecting trajectories within a specific range and a trajectory query extracting to parts of the whole trajectory. Access methods showing good performance in range queries have a shortcoming that the cost of processing trajectory Queries is high. On the other hand, trajectory-based index schemes like the TB-tree are not suitable for range queries because of high overlaps between index nodes. This paper proposes new TR(Trajectory Riving)-tree which is revised for efficiently processing the combined queries. This index scheme has several features like the trajectory preservation, the increase of the capacity of leaf nodes, and the logical trajectory riving in order to reduce dead space and high overlap between bounding boxes of nodes. In our Performance study, the number of node access for combined queries in TR-tree is about 25% less than the STR-tree and the TB-tree.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1374-1385
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• 2009

Recently, advances in speed of the CPU have for out-paced advances in memory speed. Main-memory access is increasingly a performance bottleneck for main-memory database systems. To reduce memory access speed, cache memory have incorporated in the memory subsystem. However cache memories can reduce the memory speed only when the requested data is found in the cache. We propose a new cache sensitive T-tree index structure called as $CST^*$-tree for range query search. The $CST^*$-tree reduces the number of cache miss occurrences by loading the reduced internal nodes that do not have index entries. And it supports the sequential access of index entries for range query by connecting adjacent terminal nodes and internal index nodes. For performance evaluation, we have developed a cost model, and compared our $CST^*$-tree with existing CST-tree, that is the conventional cache sensitive T-tree, and $T^*$-tree, that is conventional the range query search T -tree, by using the cost model. The results indicate that cache miss occurrence of $CST^*$-tree is decreased by 20~30% over that of CST-tree in a single value search, and it is decreased by 10~20% over that of $T^*$-tree in a range query search.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.73-83
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• 2017

In this paper, we discuss the differences between an ordinary B-tree and B-tree implemented by NTFS. There are lots of distinctions between the two B-tree, if not understand the distinctions fully, it is difficult to utilize and analyze artifacts of NTFS. Not much, actually, is known about the implementation of NTFS, especially B-tree index for directory management. Several items of B-tree features are performed that includes a node size, minimum number of children, root node without children, type of key, key sorting, type of pointer to child node, expansion and reduction of node, return of node. Furthermore, it is emphasized the fact that NTFS use B-tree structure not B+structure clearly.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.327-337
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• 2015

This work provides new forensic techinque to a hide message on a directory index in Windows NTFS file system. Behavior characteristics of B-tree, which is apoted to manage an index entry, is utilized for hiding message in slack space of an index record. For hidden message not to be exposured, we use a disguised file in order not to be left in a file name attribute of a MFT entry. To understand of key idea of the proposed technique, we describe B-tree indexing method and the proposed of this work. We show the proposed technique is practical for anti-forensic usage with a real message hiding case using a developed software tool.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• 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

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.543-552
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• 2005

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. The disk-based spatial indexing techniques, however, cannot direct apply to main memory databases, because the main purpose of disk-based techniques is to reduce the number of disk accesses. In main memory-based indexing techniques, the node access time is much faster than that in disk-based indexing techniques, because all index nodes reside in a main memory. Unlike disk-based index techniques, main memory-based spatial indexing techniques must reduce key comparing time as well as node access time. In this paper, we propose an efficient spatial index structure for main memory-based databases, called Tmr-tree. Tmr-tree integrates the characteristics of R-tree and T-tree. Therefore, Nodes of Tmr-tree consist of several entries for data objects, main memory pointers to left and right child, and three additional fields. First is a MBR of a self node, which tightly encloses all data MBRs (Minimum Bounding Rectangles) in a current node, and second and third are MBRs of left and right sub-tree, respectively. Because Tmr-tree needs not to visit all leaf nodes, in terms of search time, proposed Tmr-tree outperforms R-tree in our experiments. As node size is increased, search time is drastically decreased followed by a gradual increase. However, in terms of insertion time, the performance of Tmr-tree was slightly lower than R-tree.