• Korean Journal of Community Nutrition
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• v.23 no.2
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• pp.128-136
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• 2018

Objectives: The purpose of this study was to compare and analyse dieting practices and tendencies in 2010 and 2015 using big data. Methods: Keywords related to diet were collected from the portal site Naver from January 1, 2010 until December 31, 2010 for 2010 data and from January 1, 2015 until December 31, 2015 for 2015 data. Collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis, and seasonality analysis. Results: The results show that exercise had the highest frequency in simple frequency analysis in both years. However, weight reduction in 2010 and diet menu in 2015 appeared most frequently in N-gram analysis. In addition, keyword network analysis was categorized into three groups in 2010 (diet group, exercise group, and commercial weight control group) and four groups in 2015 (diet group, exercise group, commercial program for weight control group, and commercial food for weight control group). Analysis of seasonality showed that subjects' interests in diets increased steadily from February to July, although subjects were most interested in diets in July in both years. Conclusions: In this study, the number of data in 2015 steadily increased compared with 2010, and diet grouping could be further subdivided. In addition, it can be confirmed that a similar pattern appeared over a one-year cycle in 2010 and 2015. Therefore, dietary method is reflected in society, and it changes according to trends.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.199-214
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• 2024

Until now, research on consumers' purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers' self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.413-414
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• 2023

IT 기술의 발달로 E-Commerce 분야는 실시간으로 발생되는 데이터양이 증가하고 있으며, 발생된 데이터는 개인화 맞춤 서비스에 많이 활용되고 있다. 그러나 신생 E-commerce 기업은 신규 상품 및 기존 상품에 대한 정보와 고객 간의 상호 작용 데이터가 존재하지 않아 콜드 스타트 문제가 발생한다. 이에 본 논문에서는 E-commerce 환경에서 실시간 사용자 구매패턴 분석을 통한 사용자 상품 추천 시스템을 제안한다. 제안하는 시스템은 Kafka와 Spark를 사용해 실시간 스트림을 데이터를 처리한다. 주요 기능은 ALS 알고리즘과, FP-Growth 알고리즘을 적용해 콜트 스타트 문제를 해결하며, 사용자 구매 패턴 분석을 통한 분석 결과에 맞는 상품을 사용자에게 추천한다.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.3
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• pp.129-138
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• 2018

The purpose of this study is to analyze the daily living area of the elderly using the vast amount of travel route data collected through smart phones. In order to analyze the utilization status of the elderly into the visiting area and the living area, the subway station influence area was typed based on the number and ratio of the elderly visiting and the elderly living there. The characteristics of the elderly visiting area and the living area of the subway station area were derived by analyzing the walking route data for the three types of subway station influence areas where the elderly visit and live. First, we derive the range of visiting area and living area of the elderly near the subway station. Second, we derive the characteristic of moving distance which causes the linked walking of the elderly. Third, destination distribution and facility utilization are influenced by the subject of use, movement pattern, and facility awareness.

• The Research Journal of the Costume Culture
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• v.29 no.6
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• pp.881-903
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• 2021

This study aimed to analyze adult men's body sizes and shapes and suggest size specifications to provide preliminary data to academia and industries. A total of 814 adult men aged 30-44 were selected from the 7th Size Korea data, and 55 direct upper body measurement and calculation items were analyzed using SPSS 25.0. In individual Individual differences, thickness, circumference, and width were high, and height and length were low. Height above the waist base line and shoulder dimension decreased in early 40s age group, while height below the waist base line declined as age increased. In addition, buttocks shape changes were found in early 40s age group. According to factor analysis, 'upper body and upper-extremity horizontal size', 'torso height and upper extremity length', 'shoulder dimension', 'upper body length' and 'shoulder angle' were derived. Using clustering analysis, four different body types were classified: i) big abdomen with flat chest, ii) slender with big, raised shoulders, iii) dwarfish with small, droopy shoulders, and iv) obese with large shoulders. 'Slender with big, raised shoulders' was a typical body shape among men aged 30-44. In older participants, the 'big abdomen with flat chest' ratio was low, while 'obese with large shoulders' was more common. This study proposed size specifications by body type considering the above characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.95-106
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• 2016

Biometric data may appear different depending on the person and sasang Medicine has a close relationship with the Department. Biometric data not only mean a human heart rate, a blood pressure, a heart rate, and the past medical history, degree of aging, body mass index, but also is used as a reference measure for determining the state of health of the person. So biometric data should be reproduced for the application purposes, depending on their applications. In previous studies, because the biometric data is changed in real time and applies only to snap shut at the time of the continuity of the current time is excluded. Therefore, in this study in order to solve this problem, we propose a biometric data patton analysis model comprising a continuity of time in the big data environment consisting of biometric data. The proposed model can help determine the choice of needle position carefully when using the electronic acupuncture for care and health promotion.

• Journal of Korea Game Society
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• v.19 no.4
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• pp.27-36
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• 2019

Approaches to find hidden values using various and enormous amount of data are on the rise. As big data processing becomes easier, companies directly collects data generated from users and analyzes as necessary to produce insights. User-based data are utilized to predict patterns of gameplay, in-game symptom, eventually enhancing gaming. Accordingly, in this study, we tried to analyze the gaming strategy and user activity patterns utilizing Battlegrounds in-game data to detect the in-game hack.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.85.3-85
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• 2001

The field of forecasting is considered as an application of time-series analysis even if the data is linear or nonlinear. To obtain the forecasted values from observed data exerts a big influence on the decision-making support system or the control of machine etc. The nonlinear data appear as the random enumerated data. However we sometimes find that the pattern of past appearance repeats itself when we try to observe these data locally. From this point of view, we propose a way of forecasting nonlinear data from the pattern of past appearance using fuzzy theory. The advantages of the method are that we can forecast the next data by small numbers of previous data, and react to some differences, considering the ambiguous mature of the given data.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.49-57
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• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.