• Journal of KIISE:Databases
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• v.34 no.4
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• pp.328-342
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• 2007

We propose a new cache conscious indexing structure for processing frequently updated data efficiently. Our proposed index structure is based on a KDB-Tree, one of the representative index structures based on space partitioning techniques. In this paper, we propose a data compression technique and a pointer elimination technique to increase the utilization of a cache line. To show our proposed index structure's superiority, we compare our index structure with variants of the CR-tree(e.g. the FF CR-tree and the SE CR-tree) in a variety of environments. As a result, our experimental results show that the proposed index structure achieves about 85%, 97%, and 86% performance improvements over the existing index structures in terms of insertion, update and cache-utilization, respectively.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.119-131
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• 2009

During a decade, e-Commerce environments supporting real-time transaction processing have been getting larger. In telecommunication and financial environments, research and building for main memory database systems have been doing to support real-time transaction processing. A research on indexing for fast transaction support focuses on reducing cache misses or reducing memory access latency when cache misses happen. In the paper, we propose a prefetch method for tree index structures to reduce memory access latency. We present a prefetch-efficient pCST-tree and show superiority of the proposed tree by experiments.

• Korean Journal of Materials Research
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• v.27 no.6
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• pp.301-306
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• 2017

The discovery of new luminescent materials for use in light-emitting diodes(LEDs) has been of great interest, since LED-based solid state lighting applications are attracting a lot of attention in the energy saving and environmental fields. Recent research trends have centered on the discovery of new luminescent materials rather than on fine changes in well-known luminescent materials. In a sense, the novelty of our study beyond simple modification or improvement of existing phosphors. A good strategy for the discovery of new fluorescent materials is to introduce activators that are appropriate for conventional inorganic compounds, that have well-defined structures in the crystal structure database, but have not been considered as phosphor hosts. Another strategy is to discover new host compounds with structures that cannot be found in any existing databases. We have pursued these two strategies at the same time using composite search technology with particle swarm optimization(PSO). In this study, using PSO, we have tracked down a search space composed of Sr-Al-Si-O-N and have discovered a new phosphor structure with yellow luminescence; this material is a potential candidate for UV-LED applications.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.54-62
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• 2010

Inverted index structures have become the most efficient data structure for high performance indexing of large text collections, especially online index maintenance, In-Place and merge-based index structures are the two main competing strategies for index construction in dynamic search environments. In the above-mentioned two strategies, a contiguity of posting information is the mainstay of design for online index maintenance and query time. Whereas with the emergence of new storage device(SSD, SCRAM), those do not consider a contiguity of posting information in the design of index structures because of its superiority such as low access latency and I/O throughput speeds. However, SSD(Solid State Drive) is not well suited for traditional inverted structures due to the poor random write throughput in practical systems. In this paper, we propose the new efficient online index structure(SPM) for SSD that significantly reduces the query time and improves the index maintenance performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• Journal of The Korean Astronomical Society
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• v.47 no.3
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• pp.115-122
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• 2014

The New Vacuum Solar Telescope (NVST) is a 1-meter vacuum solar telescope that aims to observe the fine structures of active regions on the Sun. The main tasks of the NVST are high resolution imaging and spectral observations, including the measurements of the solar magnetic field. The NVST has been collecting more than 20 million FITS files since it began routine observations in 2012 and produces maximum observational records of 120 thousand files in a day. Given the large amount of files, the effective archiving and retrieval of files becomes a critical and urgent problem. In this study, we implement a new data archiving system for the NVST based on the Fastbit Not Only Structured Query Language (NoSQL) database. Comparing to the relational database (i.e., MySQL; My Structured Query Language), the Fastbit database manifests distinctive advantages on indexing and querying performance. In a large scale database of 40 million records, the multi-field combined query response time of Fastbit database is about 15 times faster and fully meets the requirements of the NVST. Our slestudy brings a new idea for massive astronomical data archiving and would contribute to the design of data management systems for other astronomical telescopes.

• 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 Korea Spatial Information System Society
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• v.10 no.3
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• pp.67-78
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• 2008

With the rapid development of wireless communication and mobile equipment, many applications for location-based services have been emerging. Moving objects such as vehicles and ships change their positions over time. Moving objects have their moving path, called the trajectory, because they move continuously. To monitor the trajectory of moving objects in a large scale database system, an efficient Indexing scheme to processed queries related to trajectories is required. In this paper, we focus on the issues of minimizing the dead space of index structures. The Minimum Bounding Boxes (MBBs) of non-leaf nodes in trajectory-preserving indexing schemes have large amounts of dead space since trajectory preservation is achieved at the sacrifice of the spatial locality of trajectories. In this thesis, we propose entry relocating techniques to reduce dead space and overlaps in non-leaf nodes. we present performance studies that compare the proposed index schemes with the TB-tree and the R*-tree under a varying set of spatio-temporal queries.

• Proceedings of the Korean Society for Information Management Conference
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• 1997.08a
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• pp.5-15
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• 1997

기존의 대부분의 정보 검색 시스템은 문서에 대한 ‘자동 색인 단계’를 거쳐 질의자의 요구에 적합한 문서들을 추출하도록 되어 있다. 이 과정에서 얼마나 적합한 문서를 빠짐없이 검색하였는가 하는 문제가, 검색 시스템의 효율성들 판단하는 데 가장 중요한 열쇠가 된다. 이 글에서는 ‘명사’ 중심의 키워드 추출이 안고 있는 몇 가지 문제점들에 관해서 논의하였다. 즉, 합성어 키워드 구축의 필요성, 동사 구문 정보에 대한 필요성, 부사구 표현에 대한 기술 필요성, 그리고 발화 상황이 고려되어야 하는 점등이 검토되었고, 이에 관한 해결책으로, 어휘정보 및 어절 정보, 나아가 구문 정보들을 담고 있는, 보다 체계적인 한국어 사전 시스템이 구축되어야 함을 강조하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.98-104
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• 2014

Locality-sensitive hashing techniques have been developed to efficiently handle nearest neighbor searches and similar pair identification problems for large volumes of high-dimensional data. This study proposes a locality-sensitive hashing method that can be applied to nearest neighbor search problems for data sets containing both numerical and categorical attributes. The proposed method makes use of dual hashing functions, where one function is dedicated to numerical attributes and the other to categorical attributes. The method consists of creating indexing structures for each of the dual hashing functions, gathering and combining the candidates sets, and thoroughly examining them to determine the nearest ones. The proposed method is examined for a few synthetic data sets, and results show that it improves performance in cases of large amounts of data with both numerical and categorical attributes.