• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.51-63
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• 2001

In this paper, we design and implement a XML document retrieval system to support structure-based retrieval using the BRS/Search system that is a commercial search engine, The implemented system in this paper represents the logical structure of XML documents as the directory structure of the Unix file system. In addition, we define the database schema of BRS/Search system to store documents, We also implement a ETID extractor, a structure information extractor, a storage manager and a query processor additionally to support content retrieval, structure retrieval, mixed retrieval and attribute retrieval in the BRS/Search system.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.87-101
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• 1991

The primary purpose of this study was to investigate a robust search methodology that could be used in full-text information retrieval systems. A robust search methodology is one that can be easily used by a variety of users (particularly naive users) and it will give them comparable search performance regardless of their different expertise or interests In order to develop a possibly robust search methodology, a fully functional prototype of a fuzzy knowledge based information retrieval system was developed. Also, an experiment that used this prototype information retreival system was designed to investigate the performance of that search methodology over a small exploratory sample of user queries To probe the relatonships between the possibly robust search performance and the query organization using fuzzy inference logic, the search performance of a shallow query structure was analyzes. Consequently the following several noteworthy findings were obtained: 1) the hierachical(tree type) query structure might be a better query organization than the linear type query structure 2) comparing with the complex tree query structure, the simple tree query structure that has at most three levels of query might provide better search performance 3) the fuzzy search methodology that employs a proper levels of cut-off value might provide more efficient search performance than the boolean search methodology. Even though findings could not be statistically verified because the experiments were done using a single replication, it is worth noting however, that the research findings provided valuable information for developing a possibly robust search methodology in full-text information retrieval.

• The Research Journal of the Costume Culture
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• v.9 no.3
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• pp.412-432
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• 2001

Internet search engines are used by the majority of find information on the Web. However, Web users can be often dissatisfied with the mistakes in the retrieval of ‘Fashion’ information from the Internet. The purpose of this study is to analyze the ‘Fashion’ category structure in the Internet search engines. There are 2 steps for achieving it: the first, to investigate the structures of ‘Fashion’ categories and then, to analyze the gap between ‘Fashion’ categories defined by them and extensive ‘Fashion’categories, which are approached on 2 sides of the fashion-life and fashion-business. We select 5 major search engines for the case study: Yahoo, Lycos, Naver, Hanmir, Empas, which ranked as top 5 of total search engines and potal sites in February, 2001, and retrieve ‘Fashion’ categories from the first level to the last level by using both “topics retrieval”. Eventually, we can find the problems of ‘Fashion’ category structure in search engines. Also, it is concluded with a brief perspective of ‘Fashion’ categories in the Internet search engines and the implications for the future.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.238-240
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• 2004

정보 통신 기술의 발달로 엄청난 양의 정보가 World Wide Web을 통해 저장되고 공유된다. 특히, 사용자가 WWW을 이용하여 필요한 정보를 얻고자할 때, 가장 많이 사용되는 것이 Web search engine이다. 그러나 Web search engine의 algorithm 자체의 부정확성과 악의적으로 작성된 Web page로 인해 search engine 결과가 사용자의 요구와 일치하지 못하는 문제가 발생한다. 본 논문에서는 여러 Web search algorithm 중에서 Web structure management 기법을 중심으로 문제점을 분석하고 이를 해결할 수 있는 수정된 algorithm을 제시한다. 마지막으로 제시된 algorithm이 spamming page를 filtering하는 과정을 예시하여 논증한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1769-1776
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• 2004

Effective keyword extraction is important in the information search system and there are several ways to select proper keyword in many keywords. Among them, DER Structure for AC Algorithm to search single keyword, can search multiple keywords but it has time complexity problem. In this paper, we developed a algorithm, "EDER structure" by expanding standalone search table based on DER structure search method to improve time complexity. We tested the algorithm using 500 text files and found that EDER structure is more efficient than DER structure for AC for keyword posting result and time complexity that 0.2 second for EDER and 0.6 second for DER structure,structure,

• Korean Management Science Review
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• v.32 no.4
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• pp.81-96
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• 2015

This paper models knowledge diffusion on an inter-organizational network. Based on literatures related to knowledge diffusion, the model considers critical factors that affect diffusion behavior including nodal property, relational property, and environmental property. We examine the relationships among network structure, knowledge search, and diffusion performance. Through a massive simulation runs based on the agent-based model, we find that the average path length of a network decreases a firm's cumulative knowledge stock, whereas the clustering coefficient of a firm has no significant relationship with the firm's knowledge. We also find that there is an interaction effect of network structure and the range of knowledge search on knowledge diffusion. Specifically, in a network of a larger average path length (APL) the marginal effect of search conduct is significantly greater than in that of a smaller APL.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.41-53
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• 1999

The effect of menu width and depth on the efficiency of information search and menu preference was investigated to identify an optimal menu structure for EIG which reflects the characteristics of human information processing. Information search time increased stepwisely as the menu width exceeded 6 items and linearly as the level of menu depth increased. The linear relationship between the error rate and the number of depth levels seems to be caused by the increase in the items to be remembered. When a menu structure was constructed by combining different menu depths and widths, it was observed that making the menu width wider rather than the depth deeper allows better information search. The menu structure rated as the most preferable and the easiest to user was that of pyramidal form. Such a result seems to come from its structural similarity to general categories which people get used to and implies that one should consider user preference as well as efficiency of search when he/she designs an EIG menu.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.355-356
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• 2006

MPEG-4 ER-BSAC decoder employes a full search method for maximum significance search and arithmetic decoding position search in spectral data decoding procedure. Then the search procedure have the most complexity. This paper proposes the new search method, the maximum significance tree structure, for the optimized implementation of BSAC decoder.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.261-264
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• 2001

This paper relates to a node structure of a multiway search tree and a search method using the node structure and, more particularly, to a search method for accelerating its search speed by reducing the depth of each small tree in a multi-way search tree. The proposed idea can increase the number of keys capable of being recorded on a cache line by using one pointer at a node of the multi-way search tree so that the number of branches in a network address search is also increased and thus the tree depth is reduced. As a result, this idea can accelerate the search speed and the speed of the forwarding engine and accomplish a further speed-up by decreasing required memories and thus increasing a memory rate.