• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.59-76
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• 2015

Recent wide spread of embedded devices and technology growth of broadband communication has led to rapid increase in the volume of created and managed data. As a result, data centers have to increase the storage capacity cost-effectively to store the created data. Data deduplication is one way to save the storage space by removing redundant data. This work propose IO extent based deduplication schemes called CORE-Dedup that exploits content-preserving access locality. We acquire IO traces from block device layer in virtual machine host, and compare the deduplication performance of chunking method between the fixed size and IO extent based. At multiple workload of 10 user's compile in virtual machine environment, the result shows that 4 KB fixed size chunking and IO extent based chunking use chunk index 14500 and 1700, respectively. The deduplication rate account for 60.4% and 57.6% on fixed size and IO extent chunking, respectively.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.106-106
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• 2010

오일샌드는 비투멘(bitumen), 물, 점토, 모래의 혼합체로 이루어진 비재래형 탄화수소 자원으로 세계적인 고유가 시대에 큰 관심을 받고 있는 석유자원 중 하나이다. 오일샌드는 대부분이 캐나다 앨버타주에 분포하고 있으며 주요 저류층으로는 아스바스카(Athabasca), 콜드레이크(Cold Lake) 지역의 멕머레이층(McMurray Formation), 클리어워터층(Clearwater Formation), 그랜드래피드층(Grand Rapid Formation)과 피스리버(Peace River) 지역의 블루스카이층(Bluesky Formation), 게팅층(Gathing Formation)이 있다. 오일샌드 저류층은 고생대 탄산염 기반암 위에 하성-에스츄어리에 이르는 다양한 퇴적환경에서 형성되어 매우 복잡한 지질특성이 나타난다. 오일샌드 저류층의 효율적인 개발을 위해서는 저류층의 복잡한 지질학적 특성의 이해가 반드시 필요하다. 본 연구에서 캐나다 오일샌드 시추코어 분석 DB, 물리검층 자료, 현장 및 현생 시추코어를 통하여 오일샌드 저류층의 지질특성화 정보의 도출을 시도하였다. 우선 캐나다 앨버타 전역에 분포하는 시추공의 기본 정보(표고, 위경도, 층서별 최상부 심도, 생산광구명, 광구개발업체)를 제공하는 AccuMap DB 프로그램을 이용하여 광역적인 오일샌드 저류층의 분포 특성을 이해하고자 주요층서에 대한 고지형도 및 층후도를 생산광구별로 도면화하여 분석하였다. 또한 캐나다 ENCANA사와 국제공동연구의 일환으로 확보된 크리스티나 레이크(Christina Lake)광구의 현장 시추코어를 이용하여 코어의 상세기재, 비파괴 물성측정, 입도/비투멘 함유량 분석과 같은 다양한 실내 시추코어분석 실험을 수행 중이다. 비파괴 물성측정은 현장 시추코어의 물리적/화학적 특성을 파악하고자 MSCL(Multi sensor core logger)과 XRF 코어 스캐너(X-ray fluorescence core scaner)를 통해 이루어지며, 분석결과로 시추코어의 감마밀도(gamma density), P파 속도(P-wave velocity), 전기비저항(resistivity), 대자율(magnetic susceptibility) 및 색지수의 물성과 정량적 화학조성을 측정한다. 현장 시추코어의 일부는 유기용매를 이용하여 퇴적물 내의 비투멘을 완전히 추출하고 퇴적물 입도와 저류층 비투멘 함유량 측정에 이용되었다. 현장 시료 분석 결과들은 물리검층 자료와 대비를 통하여 저류층의 지질특성을 규명하는 연구에 이용될 예정이다. 마지막으로 오일샌드의 현생 유사 퇴적환경으로 알려진 서해 경기만 조간대에서 시추코어 퇴적물을 획득하여 상세 기재하였으며, 이를 통해 오일샌드 저류층의 퇴적 모델을 제시하고자 퇴적층서 연구를 진행 중이다. 향후 오일샌드 관련 시추코어의 분석 결과들이 종합되면 기존 보다 비투멘 회수효율을 향상시킬 수 있는 정밀한 오일샌드 저류층 지질모델을 수립할 수 있을 것으로 기대된다.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.755-766
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• 2008

Recently, NAND flash memory is becoming into the spotlight as a next-generation storage device because of its small size, fast speed, low power consumption, and etc. compared to the hard disk. However, due to the distinct characteristics such as erase-before-write architecture, asymmetric operation speed and unit, disk-based systems and applications may result in severe performance degradation when directly implementing them on NAND flash memory. Especially when a B-tree is implemented on NAND flash memory, intensive overwrite operations may be caused by record inserting, deleting, and reorganizing. These may result in severe performance degradation. Although ${\mu}$-tree has been proposed in order to overcome this problem, it suffers from frequent node split and rapid increment of its height. In this paper, we propose Log-Structured B-Tree(LSB-Tree) where the corresponding log node to a leaf node is allocated for update operation and then the modified data in the log node is stored at only one write operation. LSB-tree reduces additional write operations by deferring the change of parent nodes. Also, it reduces the write operation by switching a log node to a new leaf node when inserting the data sequentially by the key order. Finally, we show that LSB-tree yields a better performance on NAND flash memory by comparing it to ${\mu}$-tree through various experiments.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.85-93
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• 2011

The Vertex Coloring Problem hasn't been solved in polynomial time, so this problem has been known as NP-complete. This paper suggests linear time algorithm for Vertex Coloring Problem (VCP). The proposed algorithm is based on assumption that we can't know a priori the minimum chromatic number ${\chi}(G)$=k for graph G=(V,E) This algorithm divides Vertices V of graph into two parts as independent sets $\overline{C}$ and cover set C, then assigns the color to $\overline{C}$. The element of independent sets $\overline{C}$ is a vertex ${\upsilon}$ that has minimum degree ${\delta}(G)$ and the elements of cover set C are the vertices ${\upsilon}$ that is adjacent to ${\upsilon}$. The reduced graph is divided into independent sets $\overline{C}$ and cover set C again until no edge is in a cover set C. As a result of experiments, this algorithm finds the ${\chi}(G)$=k perfectly for 26 Graphs that shows the number of selecting ${\upsilon}$ is less than the number of vertices n.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.65-72
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• 2015

In this paper, we proposed the color matching application based on smart phone which can help color blind people. For color blind people, the existing methods and applications supported color matching application which based on mobile. However, because the most research only showed the color value and color name through capture image of mobile camera, those cannot compare with capture image color of mobile camera and color of real object in real-time. To solve those problem, we proposed the color matching algorithm and developed the color matching application that can compare with color of mobile camera's capture image and color of real object in real-time, because the proposed application divides screen of smart phone into two parts and it show one part as capture image of smart phone camera and the other part as real-time camera image of smart phone. Color matching algorithm calculate cosine similarity using Red, Green, Blue, and Hue value of each image for real-time comparing and show matching result according to similarity value in real-time. To evaluate the performance of the proposed application, we tested a color matching experiment using the proposed application and the matching result was 98% success rate. Therefore, the proposed application will be a useful application which can help color blind people.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.367-380
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• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.157-168
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• 2011

Recently flash memory has been being utilized as a main storage device in mobile devices, and flashSSDs are getting popularity as a major storage device in laptop and desktop computers, and even in enterprise-level server machines. Unlike HDDs, on flash memory, the overwrite operation is not able to be performed unless it is preceded by the erase operation to the same block. To address this, FTL(Flash memory Translation Layer) is employed on flash memory. Even though the modified data block is overwritten to the same logical address, FTL writes the updated data block to the different physical address from the previous one, mapping the logical address to the new physical address. This enables flash memory to avoid the high block-erase cost. A flashSSD has an array of NAND flash memory packages so it can access one or more flash memory packages in parallel at once. To take advantage of the internal parallelism of flashSSDs, it is beneficial for DBMSs to request I/O operations on sequential logical addresses. However, the B-tree structure, which is a representative index scheme of current relational DBMSs, produces excessive I/O operations in random order when its node structures are updated. Therefore, the original b-tree is not favorable to SSD. In this paper, we propose AS(Always Sequential) B-tree that writes the updated node contiguously to the previously written node in the logical address for every update operation. In the experiments, AS B-tree enhanced 21% of B-tree's insertion performance.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.246-252
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• 2018

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.99-107
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• 2016

Korea has been known as volcanic disaster free area. However, recent surveying result shows that Baekdu mountain located in northernmost in the Korean peninsula is not a dormant volcano anymore. When Baekdu mountain is erupting, various damages due to the volcanic ash are expected in South Korea area. Especially, volcanic ash in the air may cause big aviation accident because it can hurt engine or gauges in the airplane. Therefore, it is a crucial issue to interrupt airplane navigation, whose route is overlapped with volcanic ash, after predicting three dimensional dispersion of volcanic ash. In this paper, we deals with 3D visualization techniques for volcanic ash dispersion prediction results. First, we introduce the data acquisition of the volcanic ash dispersion prediction. Dispersion prediction data is obtained from Fall3D model, which is volcanic ash dispersion simulation program. Next, three 3D visualization techniques for volcanic ash dispersion prediction are proposed. Firstly proposed technique is so called 'Cube in the Air', which locates the semitransparent cubes having different color depends on its particle concentration. Second technique is a 'Cube in the Cube' which divide the cube in proportion to particle concentration and locates the small cubes. Last technique is 'Semitransparent Volcanic Ash Plane', which laminates the layer, whose grids present the particle concentration, and apply the semitransparent effect. Based on the proposed techniques, the user could 3D visualize the volcanic ash dispersion prediction results upon his own purposes.