• Journal of the Korean Home Economics Association
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• v.41 no.6
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• pp.1-12
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• 2003

The purpose of this study were to analyze the anthropometric data of feet of Korean women with aging, to categorize the women's foot shapes, and to compare the shoe size according to the foot shapes in order to provide the basic information for more comfortable shoes. Subjects of this study were 181 women over age 20. They were measured with the direct measurement method and the indirect measurement method. 26 items were measured from the right foot and 6 items were taken on foot outline. Factor analysis, cluster analysis, analysis of variance, post-hoc test, and cross tabs were peformed for statistical analysis of the data by SPSS program. There were significant differences in height items, breadth items, girth items, and angle items by subjects' age. The older subjects' feet were wide and thick with big deformity on toes. The arch height of the older ones was low. This implicates that the degree of deformity on toes, the foot ratio, the foot girth, the foot breath and the arch height as well as the foot length are needed to be considered in developing comfortable shoes. Nine foot construction factors were extracted by the factor analysis of anthropometric measurements; foot size factor, heel and instep factor, malleolus lateralis factor, malleolus medialis factor, foot shape factor, shape of toes factor, heel height factor, big toe height factor, and internal factor. On the basis of the cluster analysis, three different foot shapes were categorized. Type 1 was large and wide foot with little deformity on little toe. Type 2 was medium foot with deformation of big toe, and with the lowest arch height. Type 3 was small and narrow foot with the highest arch height. Distribution of shoe size according to the foot shape was analyzed. The ball of foot breath was of wide distribution than the ball of foot girth. This implicates that girth items and breath items of the foot should be enclosed for the same foot length in the shoe sizing system.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1454-1465
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• 2011

Menswear body types are often labeled on garments (to indicate how the garments are designed to fit) with indicators of a size category such as regular, portly, and stout, athletic, or big and tall. A drop (relationships between the chest and waist girths) is related to the fit of a tailored suit. However, current standards are not designed for various drops or body types. There is not enough information of categorizing men's body shapes for the apparel sizing systems. In this article, a set of men's data from SizeUSA sizing survey was analyzed to investigate men's body shapes and drops. Factor analysis and a cluster analysis method were used to categorize men's body shapes. In the results, twenty-five variables were selected through the factor analysis and found four factors: girth factor, height factor, torso girth factor, and slope degree factor. According to the factor and cluster analysis, various body shapes were found: Slim Shape (SS - tall ectomorphy), Heavy Shape (HS - athletic, big & tall, endomorphy and mesomorphy), Slant Inverted Triangle Shape (SITS - regular, slight ectomorphy and slight mesomorphy weight range from normal to slightly overweight), Short Round Top Shape (SRTS - portly and stout, endomorphy). Body shapes were related to fitting categories. SS and HS were related to big & tall fitting category. SITS was related to regular. SRTS was related to portly and stout. Shape 1 (31%) and Shape 2 (26%) were related to current big & tall category. Shape 3 (34%) were related to regular. Shape 4 (9%) were in portly and stout category. ASTM D 6240 standard was the only available standard that presented a regular fitting category. Various drops were found within a same chest size group; however, this study revealed great variances of drops by body shape.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.73-74
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• 2015

IoT의 발달로 인해 새롭게 빅데이터와 그의 실시간 처리의 중요성이 증대되고 있다. 본 논문에서는 사물인터넷의 관제 및 데이터 처리 기능을 갖춘 SmartX-mini 센터를 통하여 NUC 클러스터의 빅데이터 처리 가능성을 제시하고, 이를 검증하기 위하여 SmartX-mini 테스트베드를 활용한다. SmartX-mini Center의 Spark 프레임워크를 이용한 실험을 통해 IoT 환경에서의 NUC 클러스터의 빅데이터 처리 가능에 대한 가능성을 검증하였다.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.241-250
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• 2023

Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies. Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis. Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword "unmanned cafe" was at the center of the keyword cluster. Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.153-161
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• 2021

Real-time access is required to handle continuous and unstructured data and should be flexible in management under dynamic state. Platform can be built to allow data collection, storage, and processing from local-server or multi-server. Although the former centralize method is easy to control, it creates an overload problem because it proceeds all the processing in one unit, and the latter distributed method performs parallel processing, so it is fast to respond and can easily scale system capacity, but the design is complex. This paper provides data collection and processing on one platform to derive significant insights from various data held by an enterprise or agency in the latter manner, which is intuitively available on dashboards and utilizes Spark to improve distributed processing performance. All service utilize dockers to distribute and management. The data used in this study was 100% collected from Kafka, showing that when the file size is 4.4 gigabytes, the data processing speed in spark cluster mode is 2 minute 15 seconds, about 3 minutes 19 seconds faster than the local mode.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.199-209
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• 2018

Recently, services provided to consumers are increasingly being combined with big data such as low-priced shopping, customized advertisement, and product recommendation. With the increasing importance of big data, the web crawler that collects data from the web has also become important. However, there are two problems with existing web crawlers. First, if the URL is hidden from the link, it can not be accessed by the URL. The second is the inefficiency of fetching more data than the user wants. Therefore, in this paper, through the Casper.js which can control the DOM in the headless brwoser, DOM event is generated by accessing the URL to the hidden link. We also propose an intelligent web crawler system that allows users to make steps to fine-tune both Structured and unstructured data to bring only the data they want. Finally, we show the superiority of the proposed crawler system through the performance evaluation results of the existing web crawler and the proposed web crawler.

• The Korean Journal of Community Living Science
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• v.27 no.spc
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• pp.665-679
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• 2016

This study examined the main factors for activating rural tourism of Jeollabuk-do using big data analysis. The tourism big data was gathered from public open data sources and social network services (SNS), and the analysis tools, 'Opinion Mining', 'Text Mining', and 'Social Network Analysis(SNA)' were used. The opinion mining and text mining analysis identified the key local contents of the 14 areas of Jeollabuk-do and the evaluations of customers on rural tourism. Social network analysis detected the relationships between their contents and determined the importance of the contents. The results of this research showed that each location in Jeollabuk-do had their specific contents attracting visitors and the number of contents affected the scale of tourists. In addition, the number of visitors might be large when their tourism contents were strongly correlated with the other contents. Hence, strong connections among their contents are a point to activate rural tourism. Social network analysis divided the contents into several clusters and derived the eigenvector centralities of the content nodes implying the importance of them in the network. Tourism was active when the nodes at high value of the eigenvector centrality were distributed evenly in every cluster; however the results were contrary when the nodes were located in a few clusters. This study suggests an action plan to extend rural tourism that develop valuable contents and connect the content clusters properly.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.13-19
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• 2016

Spark, an in-memory big-data processing framework is popular to use for real-time processing workload. Spark can store all intermediate data in the cluster memory so that Spark can minimize I/O access. However, when the resident memory of workload is larger that the physical memory amount of the cluster, the total performance can drop dramatically. In this paper, we analyse the factors of bottleneck on PageRank Application that needs many memory through experiment, and cluster the Spark with Tachyon File System for using memory to solve the factor of bottleneck and then we improve the performance about 18%.