• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.45-52
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• 2020

This paper proposes a solution for storage and retrieval problems for Resource Description Framework (RDF) data utilizing a key-value Solid State Device (SSD), considering storage, retrieval performance, and security. We propose a two-step compression algorithm to separate logical relationship and true values from RDF data-sets using the key-value SSD. This improves not only compression and storage efficiency but also storage security. We also propose a hybrid retrieval structure based on R∗-tree to enhance retrieval efficiency and implement a sort-merge join algorithm, and discuss factors affecting R∗-tree retrieval efficiency. Finally, we show the proposed approach is superior to current compression, storage, and retrieval approaches, obtaining target results faster while requiring less space, and competitive in terms of versatility, flexibility and security.

• Journal of the Korean Society for information Management
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• v.4 no.1
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• pp.3-23
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• 1987

This paper describes a question-answering system that can automatically analyze input texts and questions in Korean natural language. The particular texts used for the research were newspaper articles in the specific domain of sports news. The system consists of a set of Cobol programs and an associated set of data files containing lexicon, case grammar, linguistic rules. and data base. This system employs two retrieval functions of fact retrieval and passage retrieval. Therefore input questions can be answered in forms of either sentence or factual data.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.3
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• pp.103-109
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• 2011

With the growing popularity of digital video applications, those areas of the efficient transmit, storage management, and search technology for video data are emerging as an important core technology. To be an effective video indexing system, users need to be able to find the video segments that they want. Unfortunately, video data is difficult to manage because of its unstructured data type and large volume with linear forms. This paper proposes a shot verification using visual rhythm and video retrieval system. The shot verification is designed to detect a segment from video easily and quickly, known as shot boundaries, just by changing the visual rhythm without playing the image. Therefore, this can decrease the false detected shots and generate the unidentified shots and key frames. The retrieval system is constructed in terms of visual descriptor from the list of MPEG-7. The effectiveness of the proposed shot verification process and video retrieval system is demonstrated.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1365-1373
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• 1995

In this paper, we address the issues related to storage and retrieval of continuous media (CM)data we face in designing multimedia on-demand (MOD) storage servers. To support the two orthogonal factors of MOD server design, i.e., storage and retrieval of CM data, this paper discusses the techniques of disk layout, disk striping and real-time disk scheduling, which are integrated as a combined solution to the high- performance MOD storage subsystem. The proposed clustered striping technique enables either a multiple-disk or a parallel system to guarantee a continuous retrieval of CM data at the bandwidth required to support user playback rate by avoiding the formation of I/O bottlenecks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.122-129
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• 2014

Effective information gathering and retrieval of the most relevant web documents on the topic of interest is difficult due to the large amount of information that exists in various formats. Current information gathering and retrieval techniques are unable to exploit semantic knowledge within documents in the "big data" environment; therefore, they cannot provide precise answers to specific questions. Existing commercial big data analytic platforms are restricted to a single data type; moreover, different big data analytic platforms are effective at processing different data types. Therefore, the development of a common big data platform that is suitable for efficiently processing various data types is needed. Furthermore, users often possess more than one intelligent device. It is therefore important to find an efficient preference profile construction approach to record the user context and personalized applications. In this way, user needs can be tailored according to the user's dynamic interests by tracking all devices owned by the user.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.733-744
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• 1996

Content-based image retrieval(CBIR) is an image data retrieval methodology using characteristic values of image data those are generated by system automatically without any caption or text information. In this paper, we propose a content-based image data retrieval method using shape and color features of image data as characteristic values. For this, we present some image processing techniques used for feature extraction and indexing techniques based on trie and R tree for fast image data retrieval. In our approach, image query result is more reliable because both shape and color features are considered. Also, we how an image database which implemented according to our approaches and sample retrieval results which are selected by our system from 200 sample images, and an analysis about the result by considering the effect of characteristic values of shape and color.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.213-222
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• 2017

This paper proposes a new service design which is deep learning-based image retrieval system for product search on O2O shopping mall platform. We have implemented deep learning technology that provides more convenient retrieval service for diverse images of many products that are sold in the internet shopping malls. In order to implement this retrieval system, real data used by shopping mall companies were used as experimental data. However, result from several experiments have confirmed deterioration of retrieval performance due to data components. In order to improve the performance, the learning data that interferes with the retrieval is revised several times, and then the values of experimental result are quantified with the verification data. Using the numerical values of these experiments, we have applied them to the new service design in this system.

• Genomics & Informatics
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• v.7 no.4
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• pp.212-216
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• 2009

WebChemDB is an integrated chemical database retrieval system that provides access to over 8 million publicly available chemical structures, including related information on their biological activities and direct links to other public chemical resources, such as PubChem, ChEBI, and DrugBank. The data are publicly available over the web, using two-dimensional (2D) and three-dimensional (3D) structure retrieval systems with various filters and molecular descriptors. The web services API also provides researchers with functionalities to programmatically manipulate, search, and analyze the data.