• Journal of Korean Elementary Science Education
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• v.27 no.1
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• pp.9-22
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• 2008

The purpose of this study was to investigate the type of classification criterion and the characteristic of classification strategy that appear in pre-service elementary teachers' classification activity. The 4 tasks were developed for classification activity; button as a real things that attribute is prominent, shell as a real things that attribute is less prominent, snow flake as a picture cards that attribute is prominent, and galaxy as a picture cards that attribute is less prominent. The 5 college students who major in elementary education were selected. Data were collected by interview with participants, participants' classification recording paper, investigator's observation of participants' action observation, and videotaped that record participants' subject classification process. Result proved in this study is as following. First, pre-service elementary teachers used 4 qualitative classification criterion of feature, random field, image and secondary property, and used 2 dimension classification criterion of space and quantity. They used single quality classification criterion or combining dimension classification criterion in classification activity. Second, pre-service elementary teachers have classification strategy that apply each various classification criterion, and also classification strategy are different according to subject, but discussed that "anchor" and "priming effect" are important for effective classification. Result of this study is expected to contribute classification research and classification teaching program development.

• Journal of the Korean Home Economics Association
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• v.39 no.10
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• pp.55-67
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• 2001

The purpose of this study were 1) to find out the benefits sought factors and segment the customers of internet shopping mall, 2) to find out the store image factors of internet shopping mall, and 3) to analyze the internet shopping mall market using perceptual map of segmented groups. The questionnaires dealing with attribute dimension of internet shopping mall image, benefits sought, and demographic variables were selected from the previous studies or were developed for this study. The data from 319 respondents which were collected through the internet survey site was analyzed by factor analysis, cluster analysis, one-way ANOVA, and $X^2$-test. The results of this study were as follows: 1. Benefit sought by consumer in internet shopping malls was found to include six different factors: assortments of products, search efficiency, brand/fashionability, delivery convenience, promotion service and informativeness. 2. As a result of subdividing the consumers, four distinctive groups were formed on the basis of benefit factors: multi-benefit oriented group, convenience oriented group, brand oriented group and low-benefit oriented group. Demographic traits such as education and income level were proven to significantly differentiate the benefit segments. 3. In the structural components of internet shopping-malls image, product/information, service/convenience and economy were drawn from attribute dimensions. 4. 12 perceptual maps of internet shopping mall image were constructed and each ideal vector were drawn.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.169-175
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• 1996

In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.354-368
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• 2020

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.

• Korea Trade Review
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• v.46 no.2
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• pp.75-92
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• 2021

There is increasing awareness in the international alliance literature that the firm performance effects of the alliance portfolio go beyond the effects of the individual alliances. I enrich this nascent perspective by developing a alliance portfolio composition framework based on the alliance portfolio dimensions - underpinned by the simultaneity of quantitative and qualitative factors in international portfolio - that enhances firms' financial performance. This paper assesses the impact on firm performance of composing the alliance dimension within a firm's international alliance portfolio. In an unbalanced panel data analysis with fixed effects of the performance of 502 firms operating in the Korean manufacturing industry during 2011-2017, I test whether firm's three dimension of international alliance portfolio affect on firm financial performance. I find that the intensity of international alliance have significantly positive effect on the firm performance. And following the moderating analysis of three portfolio dimension-functional, relational, and attribute, all of each three international alliance portfolio has positive moderating effects on the relationship between the alliance intensity and firm performance. These results indicate that firms should consider and form simultaneous approaches to exploit the international alliance based on the alliance portfolio dimensions with intensity of alliance portfolio.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2011.04a
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• pp.121-126
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• 2011

This study is a preliminary study to develop design guidelines on shared spaces of apartment for healthy housing. It was tried to set the basic direction of the guideline development by comparing and analyzing related literatures based on the previously identified evaluation indicators of healthy housing of apartment. The major findings were as followings: 1) based on the research conducted before, the characters of shared spaces for healthy housing were classified as physical, mental and social dimensions. 2) According to the comparison and analysis of relevant standards and guidelines, focuses were mostly on the physical dimension, particularly on the human traffic lines of the convenience attributes. Other focuses were on the attractiveness of the apartment complex of the vitality attribute under the mental dimension, and community facilities of the residential stability attributes under the social dimension. Therefore, it was identified that it is required to take complementary measure regarding mental and social dimensions, and design concrete steps to include different attributes of each dimension, in order to develop comprehensive guidelines for shared space.

• Preventive Nutrition and Food Science
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• v.10 no.4
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• pp.372-377
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• 2005

This study was designed to examine the satisfaction of high school students with different types of foodservice management programs. The importance and the performance of foodservice management programs were evaluated based on the perceptions of high school students about food service characteristics affecting customer satisfactions. The average score of the attributes affecting the importance of school food service program was $4.27\pm0.49$ and the most important attribute was identified as 'the food safety $(4.68\pm0.67)$', followed by 'the taste of food $(4.66\pm0.65)$'. The average scores of all performance dimensions were lower than 3 point. 'Menu dimension' was rated as the lowest dimension $(2.61\pm0.89)$ and 'Food dimension $(2.79\pm0.70)$' was rated as the highest dimension. Significant differences among different types of foodservice management were perceived by respondents in the overall performance (F=40.244, p<0.001). Students who served by contract-conventional management rated significantly higher performance score on all of the performance attributes than the students served by other types of foodservice management. The results of the importance and the performance analysis present that student satisfaction is affected with the type of foodservice management programs and substantial differences lies between the perceptions of foodservice operations and students.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.129-131
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• 1999

다차원 데이터베이스에서 데이터분석을 위한 OLAP질의에 대한 응답 시간을 줄이기 위해 실체 뷰를 고려할 수 있다. 다차원 데이터베이스에서의 실체 뷰는 차원 테이블과 사실 테이블의 조인으로 구성되어 있는 조인 뷰를 형성하며 적절한 개수의 실체 뷰를 선택하는 일은 중요하다. 조인비용은 다차원 데이터베이스의 실체 뷰 선택에 있어서 가장 중요한 요소이다. 본 논문에서는 조인 비용을 구하기 위해서 실체 뷰의 계층정보를 이용하여 조인 테이블 추적하는 방법을 정형화하고 구현한다.

• Korean Institute of Interior Design Journal
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• v.23 no.1
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• pp.88-95
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• 2014

This study intended arousal of other viewpoints that deal with and understand spaces and shapes, by describing the concept of 'dimensions' into visual patterns. Above all, the core concept of spatial dimensions was defined as 'expandability'. Then, first, the 'golden ratio', 'Fibonacci sequence', and 'fractal theory' were defined as elements of each dimension by stage. Second, a 'unit cell' of one dimension as 'minimum unit particles' was set. Next, Fibonacci sequence was set as an extended concept into two dimensions. Expansion into three dimensions was applied to the concept of 'self-similarity repetition' of 'Fractal'. In 'fractal dimension', the concept of 'regularity of irregularity' was set as a core attribute. Plus, Platonic solids were applied as a background concept of the setting of the 'unit cell' from the viewpoint of 'minimum unit particles'. Third, while 'characteristic patterns' which are shown in the courses of 'expansion' of each dimension were embodied for the visual expression forms of dimensions, expansion forms of dimensions are based on the premise of volume, directional nature, and concept of axes. Expressed shapes of each dimension are shown into visually diverse patterns and unexpected formative aspects, along with the expression of relative blank spaces originated from dualism. On the basis of these results, the 'unit cell' that is set as a concept of theoretical factor can be defined as a minimum factor of a basic algorism caused by other purpose. In here, by applying diverse pattern types, the fact that meaning spaces, shapes, and dimensions can be extracted was suggested.