• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.691-695
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• 2023

Currently, the utilization of measurement results of the automated measurement system is very low and is at the level of providing only fragmentary measurement results. In this study, we are going to study a structure behavior analysis 3D display system with high precision and reliability for automated measurement data obtained by constructing big data by transmitting massive data values measured in real time to the cloud and using a Python-based algorithm. As a result of the study, as a system that can evaluate the behavior of a structure to a manager in real time, it provides analysis data in real time without significant restrictions regardless of the type of measurement data and sensor, and derived it as a 3D display. In addition, it was analyzed that the manager could grasp the behavior graph of the structure in real time and more easily judge the derivation of the weak part of the structure through data analysis. In the future, by analyzing the behavior of structures in three dimensions using past and present data, it is expected that more effective measurement results can be obtained in terms of repair, reinforcement, and maintenance of realistic structures.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.781-790
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• 2020

This study's primary purpose is to explore the psychological capital roles and personality traits of Big-Five in predicting OCB (organizational citizenship behavior) and performance of task in Indonesia's electricity sector. The data were gathered from the employees of four major cities in Indonesia, in Southeast Sulawesi, comprising 246 employees. The data were analyzed utilizing a PLS (partial least squares) based SEM (structural equation modeling) technique. The findings indicate that the psychological capital and personality traits of Big-Five relate significantly to OCB and the performance of task. Nevertheless, against our expectations, OCB does not significantly relate to the performance of task. This study also discusses the findings' further implications. In terms of practical implications, the findings of this research stipulate that psychological capital and Big-Five personality traits aimed to improve employee performance and can be most effective if specifically targeted at OCB. Given that both variables play an important role to promote OCB, caring training initiatives that focus on mutual help can be very valuable for organizational improvement. In a managerial perspective, organizations can increase OCB by conducting open communication strategies between managers and employees to further stimulate and strengthen the ability of employees to display extra-role behaviors.

• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.1
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• pp.35-45
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• 2020

This study focused on the park as a space to support the behavior of urban citizens in modern society. Modern city parks are not spaces that play a specific role but are used by many people, so their function and meaning may change depending on the user's behavior. In addition, current online data may determine the selection of parks to visit or the usage of parks. Therefore, this study analyzed the change of behavior in Yeouido Park, Yeouido Hangang Park, and Yangjae Citizen's Forest from 2000 to 2018 by utilizing a time series analysis. The analysis method used Big Data techniques such as text mining and social network analysis. The summary of the study is as follows. The usage behavior of Yeouido Park has changed over time to "Ride" (Dynamic Behavior) for the first period (I), "Take" (Information Communication Service Behavior) for the second period (II), "See" (Communicative Behavior) for the third period (III), and "Eat" (Energy Source Behavior) for the fourth period (IV). In the case of Yangjae Citizens' Forest, the usage behavior has changed over time to "Walk" (Dynamic Behavior) for the first, second, and third periods (I), (II), (III) and "Play" (Dynamic Behavior) for the fourth period (IV). Looking at the factors affecting behavior, Yeouido Park was had various factors related to sports, leisure, culture, art, and spare time compared to Yangjae Citizens' Forest. The differences in Yangjae Citizens' Forest that affected its main usage behavior were various elements of natural resources. Second, the behavior of the target areas was found to be focused on certain main behaviors over time and played a role in selecting or limiting future behaviors. These results indicate that the space and facilities of the target areas had not been utilized evenly, as various behaviors have not occurred, however, a certain main behavior has appeared in the target areas. This study has great significance in that it analyzes the usage of urban parks using Big Data techniques, and determined that urban parks are transformed into play spaces where consumption progressed beyond the role of rest and walking. The behavior occurring in modern urban parks is changing in quantity and content. Therefore, through various types of discussions based on the results of the behavior collected through Big Data, we can better understand how citizens are using city parks. This study found that the behavior associated with static behavior in both parks had a great impact on other behaviors.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.43-48
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• 2016

This study is to examine how big data collects user's information and is used; the status quo of exposures of user's information, and various measures of self-control by the user. This study is also to look their ethical issues and discuss problems of privacy concerning big data. As a way for users to self-control their information, they need to check the log-in state of web portal sites and set up their account so that customized advertisement and location information cannot be tracked. When posting a blog, the value of posting should be controlled. When becoming a member of a web site, users must check the access terms before agreement and beware of chained agreements and/or membership joins in order to control the exposure of their personal information. To prevent information abuse through big data through which user's information is collected and analyzed, all users must have the right to control, block or allow personal information. For an individual to have the right to control over his information, users must understand the concept of user's information and practice ethics accompanied by newly given roles in the Internet space, which will lead to the establishment of the sound and mature information society on the Internet.

• Smart Media Journal
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• v.6 no.3
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• pp.83-88
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• 2017

The data analysis system using Big-data is worthy to be used in various fields such as politics, traffic, natural disaster, shopping, customer management, medical care, and weather information. Particularly, the analysis of the momentum of an individual using an acceleration signal collected from a wearable device has already been widely used. However, since the data used in such a system stores only the data necessary for measuring the individual activity, it does not provide various analysis results other than the exercise amount of the individual. In this paper, I propose a system that analyzes collective behavior pattern and activity based on the acceleration signal that can be collected from personal smartphones for 24 hours a day and stored in big data. I also propose a system that sends acceleration signals and receives analysis results using standard messaging to use on various smart devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1073-1079
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• 2014

One of the techniques that are most in the spotlight today, it can be said that Big data. With Big Data, technologies already prevalent in our lives is GPS. Based on the GPS data and Big Data, in this paper, we try to analyze the pattern and path of movement of a particular target. Specific target collects the GPS data by classifying weather and grade and sex of college students, and day of the week in college students of one university. The collected data is analyzed such as movement path, movement time, pattern of repetitive behavior. And visualize it. The analysis method will be classified according to the purpose of data. By identifying relationships with other data results obtained. Based on the present study, the future, we will derive the results of the data more reliable. For this purpose, a wide range of information to be collected will additionally. Research will be developed add to such as Season, time, blood type, occupation data.

• Korean Management Science Review
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• v.32 no.3
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.