• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.7
• /
• pp.371-378
• /
• 2017

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people's opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.

• Culinary science and hospitality research
• /
• v.23 no.6
• /
• pp.1-11
• /
• 2017

The purpose of this study was to compare the cruise tours between China and U.S.A. through the semantic network analysis of big data by collecting online data with SCTM (Smart crawling & Text mining), a data collecting and processing program. The data analysis period was from January $1^{st}$, 2015 to August $15^{th}$, 2017, meanwhile, "cruise tour, china", "cruise tour, usa" were conducted to be as keywords to collet related data and packaged Netdraw along with UCINET 6.0 were utilized for data analysis. Currently, Chinese cruisers concern on the cruising destinations while American cruisers pay more attention on the onboard experience and cruising expenditure. After performing CONCOR (convergence of iterated correlation) analysis, for Chinese cruise tour, there were three clusters created with domestic destinations, international destinations and hospitality tourism. As for American cruise tour, four groups have been segmented with cruise expenditure, onboard experience, cruise brand and destinations. Since the cruise tourism of America was greatly developed, this study also was supposed to provide significant and social network-oriented suggestions for Chinese cruise tourism.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.185-194
• /
• 2015

We are living in big data. Specially, female consumers are the hottest issue. Female consumers have a great effected on consumer culture as comparing male consumers. Therefore, this study analysis characteristics of female consumers through case study and literature review. The summarized results of research are as follows. First, percentage of economically active population of unmarried female of 20s is high, so they actively spend lots of money on buying goods and so on. Second, they are ahead of the curve and follow entertainers. Third, domestic case studies(SD online buz marketing, C.S.I. Shinsegaemall project, Service center only for female consumers of Shinhan Card, Travel Service of Lotte Tour) and international case studies(Big data service of Target, ZARA, and Walmart) show that if we utilize big data, we can raise re-purchasing desire and analysis needs of female consumers and create new female consumers.

• Culinary science and hospitality research
• /
• v.23 no.7
• /
• pp.1-10
• /
• 2017

This study proposed SNS big data analysis method of food service industry in the 4th industrial revolution. This study analyzed the keyword of the fourth industrial revolution by using Google trend. Based on the data posted on the SNS from January 1, 2016 to September 5, 2017 (1 year and 8 months) utilizing the "Social Metrics". Through the social insights, the related words related to cooking were analyzed and visualized about attributes, products, hobbies and leisure. As a result of the analysis, keywords were found such as cooking, entrepreneurship, franchise, restaurant, job search, Twitter, family, friends, menu, reaction, video, etc. As a theoretical implication of this study, we proposed how to utilize big data produced from various online materials for research on restaurant business, interpret atypical data as meaningful data and suggest the basic direction of field application. In order to utilize positioning of customers of restaurant companies in the future, this study suggests more detailed and in-depth consumer sentiment as a basic resource for marketing data development through various menu development and customers' perception change. In addition, this study provides marketing implications for the foodservice industry and how to use big data for the cooking industry in preparation for the fourth industrial revolution.

• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.217-224
• /
• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.507-517
• /
• 2020

The present research intends to examine the relationship between the Big Five personality traits and leadership initiations among the marketing executives in Delhi NCR (INDIA), and seeks to uncover the predictors of leadership initiations within personality traits. The data are collected through online survey method using different social media platforms. A sample of 233 (male =136 and female =97) marketing executive's responses were included. The data collected with the help of self-reported Big Five model inventory and leadership initiation test. The collected data were analyzed statistically by using descriptive statistics, correlation. and stepwise multiple regression analysis. The results revealed that the age of respondents inversely correlated with leadership initiation. Neuroticism revealed significant inverse correlation with leadership initiation, whereas significant positive correlations were found between extraversion, conscientiousness, agreeableness, and leadership initiations, while openness to experience revealed insignificant positive correlation with leadership initiation. Extraversion and conscientiousness appeared as the most dominant personality traits among marketing executives, irrespective of gender, that positively influenced leadership initiation and appeared as the predictor of leadership initiation. In male executives extraversion and age emerged as the predictors of leadership behavior, while in female executives extraversion and openness to experience personality traits appeared as the predictors of leadership initiation.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.359-364
• /
• 2021

In recent years Hadoop usage has been increasing day by day. The need of development of the technology and its specified outcomes are eagerly waiting across globe to adopt speedy access of data. Need of computers and its dependency is increasing day by day. Big data is exponentially growing as the entire world is working in online mode. Large amount of data has been produced which is very difficult to handle and process within a short time. In present situation industries are widely using the Hadoop framework to store, process and produce at the specified time with huge amount of data that has been put on the server. Processing of this huge amount of data having small files & its storage optimization is a big problem. HDFS, Sequence files, HAR, NHAR various techniques have been already proposed. In this paper we have discussed about various existing techniques which are developed for accessing and storing small files efficiently. Out of the various techniques we have specifically tried to implement the HDFS- HAR, NHAR techniques.

• The Journal of KAIRB
• /
• v.4 no.1
• /
• pp.16-22
• /
• 2022

Purpose: The traditional ethical study only suggests a blurred insight on the research using medical big data, especially in this rapid-changing and demanding environment which is called "4th Industry Revolution." Current institutional/ethical issues in big data research need to approach with the thoughtful insight of past ethical study reflecting the understanding of present conditions of this study. This study aims to examine the ethical issues that are emerging in recent health care big data research. So, this study aims to survey the public perceptions on of health care big data as part of the process of public discourse and the acceptance of the utility and provision of big data research as a subject of health care information. In addition, the emerging ethical challenges and how to comply with ethical principles in accordance with principles of the Belmont report will be discussed. Methods: Survey was conducted from June 3th August to 6th September 2020. The online survey was conducted through voluntary participation through Internet users. A total of 319 people who completed the survey (±5.49%P [95% confidence level] were analyzed. Results: In the area of the public's perspective, the survey showed that the medical information is useful for new medical development, but it is also necessary to obtain consents from subjects in order to use that medical information for various research purposes. In addition, many people were more concerned about the possibility of re-identifying personal information in medical big data. Therefore, they mentioned the necessity of transparency and privacy protection in the use of medical information. Conclusion: Big data on medical care is a core resource for the development of medicine directly related to human life, and it is necessary to open up medical data in order to realize the public good. But the ethical principles should not be overlooked. The right to self-determination must be guaranteed by means of clear, diverse consent or withdrawal of subjects, and processed in a lawful, fair and transparent manner in the processing of personal information. In addition, scientific and ethical validity of medical big data research is indispensable. Such ethical healthcare data is the only key that will lead to innovation in the future.

• Korean Journal of Health Education and Promotion
• /
• v.30 no.3
• /
• pp.59-73
• /
• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.19-28
• /
• 2021

Korea is promoting a digital new deal policy for the digital transformation and innovation accelerating of the industry. However, because of the strict existing data-related laws, there are still restrictions on the industry's use of data for the digital new deal policy. In order to solve this issue, a revised bill of the Data 3 Act has been proposed, but there is still insufficient discussion on how it will actually affect the activation of data use in the industry. Therefore, this study aims to analyze the perception of public opinion on the Data 3 Act and the implications of the revision of the Data 3 Act. To this end, the revision of the Data 3 Act and related research trends were analyzed, and the perception of the Data 3 Act was analyzed using a big data analysis technique. According to the analysis results, while promoting the vitalization of the data industry in line with the purpose of the revision, the Data 3 Act has a concern that it focuses on specific industries. The results of this study are meaningful in providing implications for future improvement plans by analyzing online perceptions of the industrial impact of the Data 3 Act in the early stages of implementation through big data analysis.