• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.11
• /
• pp.2027-2032
• /
• 2016

The rapid diffusion of smart phones and the development of data storage and analysis technology have made the field of big-data a promising industry in the future. In the marketing field, big-data analysis on social data can be used for understanding the needs of consumers as an effective and efficient marketing tool. Before the age of big-data, companies had relied upon the traditional methods such as questionnaire survey and marketing test in which a small number of consumers had participated. The traditional methods have still been used. Although both of big-data analysis and traditional methods are useful to understand consumers. It is need to check whether the results from both include similar implications. In this point, this study attempts to compare the results of big-data analysis with that of questionnaire survey on some cosmetics brands methods. As the results of this study, both results of big-data analysis and questionnaire survey include similar implications.

• Information Systems Review
• /
• v.21 no.4
• /
• pp.175-195
• /
• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.35 no.1
• /
• pp.19-28
• /
• 2019

The purpose of this study is to evaluate the walking behavior analysis methodology used in the previous studies, paying attention to the demand for empirical data collecting for urban and neighborhood planning. The preceding researches are divided into (1)Recording, (2) Surveys, (3)Statistical data, (4)Global positioning system (GPS) devices, and (5)Mobile Big Data analysis. Next, we analyze the precedent research and identify the changes of the walkability research. (1)being required empirical data on the actual walking and moving patterns of people, (2)beginning to be measured micro-walking behaviors such as actual route, walking facilities, detour, walking area. In addition, according to the trend of research, it is analyzed that the use of GPS device and the mobile big data are newly emerged. Finally, we analyze pedestrian data based on mobile big data in terms of 'application' and distinguishing it from existing survey methodology. We present the possibility of mobile big data. (1)Improvement of human, temporal and spatial constraints of data collection, (2)Improvement of inaccuracy of collected data, (3)Improvement of subjective intervention in data collection and preprocessing, (4)Expandability of walking environment research.

• Convergence Security Journal
• /
• v.22 no.1
• /
• pp.55-60
• /
• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• Culinary science and hospitality research
• /
• v.23 no.8
• /
• pp.195-205
• /
• 2017

The purpose of this study was to compare the Starbucks in South Korea with Starbucks in U.S.A through the semantic network analysis of big data by collecting online data with SCTM(Smart Crawling & Text Mining) program which was developed by big data research institute at Kyungsung University, a data collecting and processing program. The data collection period was from January 1st 2014 to December 7th 2017, and packaged Netdraw along with UCINET 6.0 were utilized for data analysis and visualization. After performing CONCOR(convergence of iterated correlation) analysis and centrality analysis, this study illustrated the current characteristics of Starbucks for Korea and U.S.A reflected by the social network and the differences between Korea and U.S.A. Since the Starbucks was greatly developed, especially in Korea. this study also was supposed to provide significant and social-network oriented suggestions for Starbucks USA, Starbucks Korea and also the whole coffee industry. Also this study revealed that big data analytics can generate new insights into variables that have been extensively studied in existing hospitality literature. In addition, implications for theory and practice as well as directions for future research are discussed.

• Journal of Korean Society for Quality Management
• /
• v.45 no.4
• /
• pp.677-696
• /
• 2017

Purpose: This study suggests that Six Sigma MBB should be used as a key talent to lead the fourth industrial revolution era by training them with big data processing capability. Methods: Through the analysis between articles on the fourth industrial revolution and Six Sigma related papers, common competencies of data scientists and Six Sigma MBBs were identified and the big data analysis capabilities needed for Six Sigma MBB were derived. Then, training was conducted to improve the big data analysis capabilities so that Six Sigma MBB is able to design algorithms required in the fourth industrial revolution era. Results: Six Sigma MBBs, equipped with the knowledge in field site improvement and basic statistics, were provided with 40 hours of big data analysis training and then were made to design a big data algorithm. Positive results were obtained after applying a AI algorithm which could forecast process defects in a field site. Conclusion: Six Sigma MBB equipped with big data capability will make the best talent for the fourth industrial revolution era. A Six Sigma MBB has an excellent capability for improving field sites. Utilizing the competencies of MBB can be a key to success in the fourth industrial revolution. We hope that the results of this study will be shared with many companies and many more improved case studies will arise in the future as a result of this study.

• The Korean Journal of Applied Statistics
• /
• v.27 no.2
• /
• pp.169-183
• /
• 2014

Massive and various types of data recorded everywhere are called big data. Therefore, it is important to analyze big data and to nd valuable information. Besides, to standardize unstructured big data is important for the application of statistical methods. In this paper, we will show how to standardize unstructured big data using MapReduce which is a distribution processing system. We also apply simple correspondence analysis and multiple correspondence analysis to nd the relationship and characteristic of direct relationship words for Samsung Electronics and The Korea Economic Daily newspaper as well as Apple Inc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.129-130
• /
• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of the Korea Convergence Society
• /
• v.8 no.12
• /
• pp.265-274
• /
• 2017

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.12
• /
• pp.101-108
• /
• 2017

The study proposed a system that filters the data that is entered when analyzing big data such as SNS and BLOG. Personal information includes impersonal personal information, but there is also personal information that distinguishes it from personal information, such as religious institution, personal feelings, thoughts, or beliefs. Define these personally identifiable information as sensitive information. In order to prevent this, Article 23 of the Privacy Act has clauses on the collection and utilization of the information. The proposed system structure is divided into two stages, including Big Data Processing Processes and Sensitive Information Filtering Processes, and Big Data processing is analyzed and applied in Big Data collection in four stages. Big Data Processing Processes include data collection and storage, vocabulary analysis and parsing and semantics. Sensitive Information Filtering Processes includes sensitive information questionnaires, establishing sensitive information DB, qualifying information, filtering sensitive information, and reliability analysis. As a result, the number of Big Data performed in the experiment was carried out at 84.13%, until 7553 of 8978 was produced to create the Ontology Generation. There is considerable significan ce to the point that Performing a sensitive information cut phase was carried out by 98%.