• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.615-623
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• 2006

A non-functional requirement is a property or quality that the proposed systems have to support the functional requirements. A non-functional requirement is reflected by quality attribute These non-functional requirements playa crucial role during system development, serving as selection criteria for choosing among decisions. It should be continuously considered through the software development process. In spite of the importance of the non-functional requirements, it received little attention because of ambiguousness and invisibility of non-functional requirements. Therefore non-functional model which is a process to analyze the non-functional requirement is proposed for improving the management efficiency of non-functional requirements. Also, this paper presents the trace among the UML diagrams to the conceptual model. According to the non-functional requirement development process, this paper achieved performance-based case study. After then, non-functional requirement should be traced using the UML diagrams.

• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.37-60
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• 2010

The purpose of this study is to investigate image search query reformulation patterns in relation to image attribute categories. A total of 592 sessions and 2,445 queries from the Excite Web search engine log data were analyzed by utilizing Batley's visual information types and two facets and seven sub-facets of query reformulation patterns. The results of this study are organized with two folds: query reformulation and categorical transition. As the most dominant categories of queries are specific and general/nameable, this tendency stays over various search stages. From the perspective of reformulation patterns, while the Parallel movement is the most dominant, there are slight differences depending on initial or preceding query categories. In examining categorical transitions, it was found that 60-80% of search queries were reformulated within the same categories of image attributes. These findings may be applied to practice and implementation of image retrieval systems in terms of assisting users' query term selection and effective thesauri development.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.134-143
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• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• Journal of KIISE
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• v.42 no.5
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• pp.558-565
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• 2015

Recently, the number of new malware and malware variants has dramatically increased. As a result, the time for analyzing malware and the efforts of malware analyzers have also increased. Therefore, malware classification helps malware analyzers decrease the overhead of malware analysis, and the classification is useful in studying the malware's genealogy. In this paper, we proposed a set of key opcode to classify the malware. In our experiments, we selected the top 10-opcode as key opcode, and the key opcode decreased the training time of a Supervised learning algorithm by 91% with preserving classification accuracy.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.539-547
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• 2019

This study conducted an importance-satisfaction analysis on choice attributes of cosmetics targeting consumers of road shop cosmetics and suggested improvements needed to increase consumer satisfaction. A summary of the study's results is as follows. First, as to the importance of choice attribute items of road shop cosmetics, skin compatibility appeared to be the highest. For satisfaction as well, skin compatibility appeared to be the highest. Second, when the differences between importance and satisfaction of choice attributes of road shop cosmetics were analyzed, among a total of 15 choice factors, differences appeared in 6 factors. Among them, 5 attributes such as the product function and skin compatibility, excluding the salesperson's service, appeared to have higher importance than satisfaction. Third, based on the IPA results, the study analyzed which factors should be maintained or improved and accordingly suggested efficient resource allocation strategies and marketing strategies that can be practically applied.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.149-155
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• 2022

This study checks how the managerial competency of a single beauty company affects management performance, and the managerial competency of a single beauty company has a moderating effect of a beauty education institution between management performance and presents related implications. As for the survey for the study, direct surveys and online surveys were conducted from May 1 to May 31, 2021, and a total of 218 copies were used for empirical analysis. As a result of the analysis, the managerial competence of a single beauty company was divided into three factors: psychological characteristics, technical characteristics, and management ability, and all factors influenced management performance. In addition, it was analyzed that the managerial competency of a single beauty company in beauty education institutions has a moderating effect in the relationship between management performance. Therefore, managers of single-person beauty companies should make various efforts to improve managerial capabilities that affect management performance, and above all, it is important to select educational institutions necessary for each reverse direction.

• Korean Journal of Community Nutrition
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• v.28 no.4
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• pp.329-339
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• 2023

Objectives: This study aims to explore consumer awareness and usage patterns of local food in Jecheon city during the COVID-19 pandemic, contributing to the establishment of the Jecheon City Food Plan. Methods: Surveys were conducted from July 24 to August 24, 2021, using a combination of web-link and self-administered methods for adults living in Jecheon city (n = 250). Descriptive analysis, t-test, importance and satisfaction analysis (ISA) of local food choice attributes were performed using SPSS Statistics. Results: Participants prioritized freshness when purchasing agricultural products. The freshness of Jecheon local food was the selection attribute with the highest consumer satisfaction and could provide purchase motivation. Approximately 73.6% of respondents understood the concept of local food, and 70% were familiar with Jecheon's local food. Notably, 94.8% expressed an intention to purchase but held negative views on selling local food in other areas. The need to increase the supply of local food to vulnerable populations and public school catering was highly recognized. The ISA identified 'affordable price', 'delivery service', and 'product information' as areas requiring improvement. On the other hand, 'freshness of products', 'quality for the price', and 'support for local farmers and economy' were identified as attributes to be maintained and strengthened. Conclusions: Consumers in Jecheon city recognized local foods as more than just 'consumer goods'. Our findings suggest the need for further research on local food revitalization and more comprehensive local food planning to enhance consumer satisfaction.

• The Korean Journal of Franchise Management
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• v.7 no.1
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.133-142
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• 2010

To monitor composition and change of the national land, intelligent topographical classifier which enables accurate classification of land-cover types from airborne LiDAR data is highly required. We developed a topographical classifier development support system cooperating with da1a mining tool WEKA to help users to construct accurate topographical classification systems. The topographical classifier development support system has the following functions; superposing LiDAR data upon corresponding aerial images, dividing LiDAR data into tiles for efficient processing, 3D visualization of partial LiDAR data, feature from tiles, automatic WEKA input generation, and automatic C++ program generation from the classification rule set. In addition, with dam mining tool WEKA, we can choose highly distinguishable features by attribute selection function and choose the best classification model as the result topographical classifier. Therefore, users can easily develop intelligent topographical classifier which is well fitted to the developing objectives by using the topographical classifier development support system.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.175-181
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• 2014

Korean government proposed a new initiative 'government 3.0' with which the administration will open its dataset to the public before requests. City of Seoul is the front runner in disclosure of government data. If we know what kind of attributes are governing factors for any given segmentation, these outcomes can be applied to real world problems of marketing and business strategy, and administrative decision makings. However, with respect to city of Seoul, selection of optimal variables from the open dataset up to several thousands of attributes would require a humongous amount of computation time because it might require a combinatorial optimization while maximizing dissimilarity measures between clusters. In this study, we acquired 718 attribute dataset from Statistics Korea and conducted an analysis to select the most suitable variables, which differentiate Gangnam from other districts, using the Genetic algorithm and Dunn's index. Also, we utilized the Microsoft Azure cloud computing system to speed up the process time. As the result, the optimal 28 variables were finally selected, and the validation result showed that those 28 variables effectively group the Gangnam from other districts using the Ward's minimum variance and K-means algorithm.