• Journal of Korean Library and Information Science Society
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• v.30 no.3
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• pp.427-456
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• 1999

This paper deals with the problems of information retrieval through full-test database which arise from both the deficiency of searching strategies or methods by information searcher and the difficulties of query representation, generation, extension, etc. In oder to solve these problems, we should use automatic retrieval instead of manual retrieval in the past. One of the ways to make the gap narrow between the terms by the writers and query by the searchers is that the query should be searched with the terms which the writers use. Thus, the preconditions which should be taken one accorded way to solve the problems are that all areas of information retrieval such as should taken one accorded way to solve the problems are that all areas of information retrieval such as contents analysis, information structure, query formation, query evaluation, etc. should be solved as a coherence way. We need to deal all the ares of automatic information retrieval for the efficiency of retrieval thought this paper is trying to solve the design of thesaurus browser. Thus, this paper shows the theoretical analyses about the form of information retrieval, automatic indexing, clustering technique, establishing and expressing thesaurus, and information retrieval technique. As the result of analyzing them, this paper shows us theoretical model, that is to say, the thesaurus browser by clustering algorithm. The result in the paper will be a theoretical basis on new retrieval algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.799-803
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• 2010

The KDB-tree is a traditional indexing scheme for retrieving multidimensional data. Much research for KDB-tree family frequently addresses the low storage utilization and insufficient retrieval performance as their two bottlenecks. The bottlenecks occur due to a number of unnecessary splits caused by data insertion orders and data skewness. In this paper, we propose a novel index structure, called as $KDB_{CS}^+$-tree, to process skewed data efficiently and improve the retrieval performance. The $KDB_{CS}^+$-tree increases the number of fan-outs by exploiting bit-vectors for representing splitting information and pointer elimination. It also improves the storage utilization by representing entries as a hierarchical structure in each internal node.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.1-16
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• 2005

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. Accordingly, this study suggests the re-ranking retrieval model which reflects the content based similarity between user's inquiry terms and index words by grasping the document knowledge structure. In order to accomplish this, the former constructs a thesaurus and similarity relation matrix to provide the subject analysis mechanism and the latter propose the algorithm which establishes a search model such as query expansion in order to analyze the user's demands. Therefore, the algorithm that this study suggests as retrieval utilizing the information structure of a retrieval system can be content-based retrieval mechanism to establish a 2-step search model for the preservation of recall and improvement of accuracy which was a weak point of the previous fuzzy retrieval model.

• International Journal of Contents
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• v.1 no.2
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• pp.39-43
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• 2005

In this paper, we describe a generation mechanism of semantic-based queries for high accuracy information retrieval and question answering. It is difficult to offer the correct retrieval result because general information retrieval systems do not analyze the semantic of user's natural language question. We analyze user's question semantically and extract semantic features, and we .generate semantic-based queries using them. These queries are generated using the se-mantic-based question analysis grammar and the query generation rule. They are represented as semantic features and grammatical morphemes that consider semantic and syntactic structure of user's questions. We evaluated our mechanism using 100 questions whose answer type is a person in the TREC-9 corpus and Web. There was a 0.28 improvement in the precision at 10 documents when semantic-based queries were used for information retrieval.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.31-44
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• 2024

The inverted index is combination of the keywords and posting lists associated for indexing of document. In modern age excessive use of technology has increased data volume at a very high rate. Big data is great concern of researchers. An efficient Document indexing in big data has become a major challenge for researchers. All organizations and web engines have limited number of resources such as space and storage which is very crucial in term of data management of information retrieval system. Information retrieval system need to very efficient. Inverted indexing technique is introduced in this research to minimize the delay in retrieval of data in information retrieval system. Inverted index is illustrated and then its issues are discussed and resolve by implementing the scalable inverted index. Then existing algorithm of inverted compared with the naïve inverted index. The Interval list of inverted indexes stores on primary storage except of auxiliary memory. In this research an efficient architecture of information retrieval system is proposed particularly for unstructured data which don't have a predefined structure format and data volume.

• ETRI Journal
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• v.22 no.2
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• pp.32-42
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• 2000

The existing multi-dimensional index structures are not adequate for indexing higher-dimensional data sets. Although conceptually they can be extended to higher dimensionalities, they usually require time and space that grow exponentially with the dimensionality. In this paper, we analyze the existing index structures and derive some requirements of an index structure for content-based image retrieval. We also propose a new structure, for indexing large amount of point data in a high-dimensional space that satisfies the requirements. in order to justify the performance of the proposed structure, we compare the proposed structure with the existing index structures in various environments. We show, through experiments, that our proposed structure outperforms the existing structures in terms of retrieval time and storage overhead.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.525-530
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• 2008

• 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.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.305-312
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• 2001

Recently, high-dimensional index structures have been required for similarity search in such database applications s multimedia database and data warehousing. In this paper, we propose a new cell-based signature tree, called CS-tree, which supports efficient storage and retrieval on high-dimensional feature vectors. The proposed CS-tree partitions a high-dimensional feature space into a group of cells and represents a feature vector as its corresponding cell signature. By using cell signatures rather than real feature vectors, it is possible to reduce the height of our CS-tree, leading to efficient retrieval performance. In addition, we present a similarity search algorithm for efficiently pruning the search space based on cells. Finally, we compare the performance of our CS-tree with that of the X-tree being considered as an efficient high-dimensional index structure, in terms of insertion time, retrieval time for a k-nearest neighbor query, and storage overhead. It is shown from experimental results that our CS-tree is better on retrieval performance than the X-tree.

• Atmosphere
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• v.17 no.1
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• pp.69-86
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• 2007

The characteristics of the dual-Doppler wind retrieval method based on a three dimensional variational (3DVAR) conception were investigated from the following four points of view; the sensitivity of the number of iteration, the effect of the weak constraint term, the effect of the smoothness term, and the sensitivity of the error mixing ratio of the radial velocities. In the experiment, the radial velocities relative to the Gosan and Jindo radar sites of the Korea Meteorological Administration (KMA) were calculated from the forecasting of the WRF (Weather Research and Forecast; Skamarock, 2004) model at 1330 UTC 30 June 2006, which is the one and half hour forecast from the initial time, 1200 UTC on that day. The results showed that the retrieval performance of the horizontal wind field was robust, but that of the vertical wind was sensitive to the external conditions, such as iteration number and the on/off of the weak constraint term. The sensitivity of error mixing ratio was so large that even the horizontal wind retrieval efficiency was reduced a lot. But the sensitivity of the smooth term was not so large. When we applied this method to the real mesoscale convective system (MCS) between the Gosan and Jindo radar pair at 1430 UTC 30 June 2006, the wind structure of the convective cells in the MCS was consistently retrieved relative to the reflectivity factor structure. By comparing the vertical wind structure of this case with that of 10 minutes after, 1440 UTC 30 June 2006, we got the physical consistency of our method.