• Journal of the Korean Society for Library and Information Science
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• v.32 no.4
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• pp.209-225
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• 1998

This research focuses on how users behave when they search by subject using online public access catalog(OPAC). Major findings are as follows. 1)Main access poults are subject field$(55.2\%)$and title field$(42.2\%)$. 2) The search failure rate in subject searching is $59.3\%$. 3) Ma]or reasons for subject search failures are two-fold : use of inappropriate search terms $(48.5\%)$ and non-use of Boolean Operators$(42.5\%)$. 4) In order to overcome search failures users tend to change originally used search terms$(42.0\%)$ and search fields$(33.8\%) into different ones.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.79-88
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• 2015

The web search engines are exposed to the issue of uncertainty because of ambiguous queries, being input for retrieving the accurate results. Ambiguous queries constitute a significant fraction of such instances and pose real challenges to web search engines. Moreover, web search has created an interest for the researchers to deal with search by considering context in terms of location perspective. Our proposed disambiguation approach is designed to improve user experience by using context in terms of location relevance with the document relevance. The aim is that providing the user a comprehensive location perspective of a topic is informative than retrieving a result that only contains temporal or context information. The capacity to use this information in a location manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or clustering based on location. In order to carry out the approach, we developed a Java based prototype to derive the contextual information from the web results based on the queries from the well-known datasets. Among those results, queries are further classified in order to perform search in a broad way. After the result provision to users and the selection made by them, feedback is recorded implicitly to improve the web search based on contextual information. The experiment results demonstrate the outstanding performance of our approach in terms of precision 75%, accuracy 73%; recall 81% and f-measure 78% when compared with generic temporal evaluation approach and furthermore achieved precision 86%, accuracy 71%; recall 67% and f-measure 75% when compared with web document clustering approach.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.57-65
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• 2004

In the block matching algorithm, search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image quality. In this paper, we propose a new fast block matching algorithm using the unit-diamond search pattern and the flat-hexagon search pattern. Our algorithm first finds the motion vectors that are close to the center of search window using the unit-diamond search pattern, and then fastly finds the other motion vectors that are not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the hexagon-based search algorithm(HEXBS), the proposed unit-diamond and flat-hexagonal pattern search algorithm(UDFHS) improves as high as 11∼51% in terms of average number of search point per motion vector estimation and improves about 0.05∼0.74㏈ in terms of PSNR(Peak Signal to Noise Ratio).

• Journal of the Korean Society for Library and Information Science
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• v.30 no.2
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• pp.51-86
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• 1996

The primary purpose of this study is to verify the difference of behavioural characteristics between the subject specialists and professional searchers in searching full-text databases. The major findings and conclusions from this study are summarized as follows. Analyses of Search questions(the degree of understanding with search questions, the degree of difficulty in selecting terms, and the degree of expectation of search results), search processes(the number of search terms used, the number of Boolean operators and qualifiers used, the number of documents browsed and the search time(the connecting time, time to spend per one output document, time to spend per one relevant output document) and search results(the searching efficiency(the number of relevant documents, the ,recall ratio and the precision ratio), the search cost(the total search cost. the search cost per one output document and the search cost per one relevant output document) and the degree of satisfaction with search results) are significantly different between the subject specialists and professional searchers in searching full-text databases.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.165-170
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• 2015

SNOMED CT browser is a browsing tool for searching clinical terms in SNOMED CT which is a standard terminology set used worldwide. The search result view of previous browsers merely list up candidate terminologies. The problem is that most of users become confused about how to select an appropriate term from the list. This leads serious waste of medical recoding cost. This paper discusses characteristics of SNOMED CT dataset and proposes a novel design of enhanced result view by restructuring the results using relationships of SNOMED CT concepts. Using the proposed scheme, medical doctors or officers can select appropriate terms more efficiently and can reduce overall recording time.

• Journal of Information Management
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• v.24 no.4
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• pp.43-57
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• 1993

This paper discusses the basic concept of artificial intelligence(AI) and expert system and a particular technique(fuzzy logic) applied to expert systems. It examines expert system as search intermediaries during the past few years, particularly in terms of the following functions: 1) handling certain classes of questions on a particular database, 2) assisting in decision making for selecting databases or search terms, and 3) offering advice while keeping the end-user in the control of the searching process. The limitations and difficulties involved in developing such expert systems are also presented.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.19-37
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• 2016

Advance of the Internet environment is applied not only to information search but also to the area of consumption behavior. Current research analyzes online use behavior and online information search of consumers in terms of users' perception. With the result of the research, it is noticed that promotion focus brings broader variation of information use behavior, and utilitarian value has a beneficial impact on the online exploratory information search. In addition, it is revealed that the more exploratory the information search is, the wider the range of online shopping information search is. Finally, people who have utilitarian shopping value showed more exploratory behavior in online search, especially for the search of informational products, than those who have hedonic shopping value. Present research is believed to improve practical influence of consumers' personality on online use behavior when customers purchase search products online. As a result, it would contribute to consumer research and marketing held online.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.126-134
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• 1980

In this paper, Srivastava's Theorem for the search design is considered, with additional assumptions, to the $3^n$ parallel flats fractions. It is also expressed in terms of ACPM.