• International Journal of Contents
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• v.17 no.3
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• pp.74-83
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• 2021

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.681-689
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• 2019

The 46th Davos Forum of the World Economic Forum (WEF) predicts the continued growth of the 4th industry in the future. Currently, the 4th industry is attracting attention in various academic and practical fields. As a core technology of the 4th industry, Big Data is regarded as a major resource to lead the 4th industrial revolution along with artificial intelligence. As the growing interest in Big Data, researches on it are actively being done. However, literature studies on existing Big Data are focused on qualitative research, and quantitative research is insufficient. Therefore, this study aims to analyze the big data research flow in MIS field and to make academic thirst for quantification. This study has collected 145 abstracts of big data papers published in major journals in MIS field and confirmed that a majority of papers are published in Decision Support Systems Journal. Text mining and text network analysis were performed only for DSS journals to eliminate bias. As a result of the analysis, it was found out that researches on combining big data in the management field between 2012 and 2014, and researches on system development and analysis method for using big data from 2015 to 2017 were conducted.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.79-88
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• 2022

Social media big data includes a lot of information that can identify not only consumer consumption patterns but also local images. This paper was collected annually data including 'Samcheok' from 2015 to 2019 from Blog and Cafe of Naver and Daum in domestic portal site, and analyzed the regional image change after refining keyword which forms the regional image by performing text mining and network analysis. According to the research results, the regional image of 2015 was expressed with image cognitive elements of the nearby place name or place etc. such as 'Jangho Port', 'Donghae', and 'Beach'. However the regional image both 2016 and 2019 were changed with image cognitive elements of 'SamcheokSolbich' which is a special place within region. Therefore as the keywords related to the local image include 'Jangho Port' and Resort, which are the representative attractions of Samcheok, it can be seen that the infrastructure factor plays a big role in forming the local image. The significance test for the network data used the bootstrap technique, and the p-values in 2015, 2016, and 2019 were 0.0002, 0.0006, and 0.0002, respectively, which were found to be statistically significant at the significance level of 5%.

• Journal of East Asia Management
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• v.4 no.1
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• pp.1-22
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• 2023

This study conducted a big data analysis study through text mining and semantic network analysis to explore the perception of defense reform 2.0. The collected data were analyzed with the top 70 keywords as the appropriate range for network visualization. Through word frequency analysis, connection centrality analysis, and an N-gram analysis, we identified issues that received much attention such as troop reduction, shortening of military service period, dismantling of the border area unit, and returning wartime operational control. In particular, the results of clustering words through CONCOR analysis showed that there was a great interest in pursuing the technical group, concerns about military capacity reduction, and reorganization of manpower structure. The results of the analysis through text mining techniques are as follows. First, it was found that there was a lack of awareness about measures to reinforce the reduced troops while receiving much attention to the reduction of troops in Defense Reform 2.0. Second, it was found that it is necessary to actively communicate with the local community due to the deconstruction and movement of the border area units, such as the decrease of the population of the region and the collapse of the local commercial area. Third, it was judged that it is necessary to show substantial results through the promotion of barracks culture and the defense industry, which showed that there was less interest than military structure and defense operation from the people and the introduction of active policies. Through this study, we analyzed the public's interest in defense reform 2.0, which is a representative defense policy, and suggested a plan to draw support for national policy.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• Journal of Information Technology Services
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• v.19 no.4
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• pp.1-11
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• 2020

Through text mining, sentiment analysis, and semiotics analysis, this study aims to reinterpret the meaning of user emotional words and related words to derive strategic elements of brand and design. After selecting a local car manufacturer whose user opinion on the brand is a clear topic, web-crawl the car comments of the manufacturer directly created by the users online. Then, analyze the extracted morphology and its associated words and convert them to fit the marketing mix theory. Through this process, propose a methodology that allows consumers to supplement and improve brand elements with negative sensibilities, and to inherit elements with positive sensibilities and manage brands reasonably. In particular, the Map presented in this study are considered to be fully utilized as information for overall brand management.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.93-94
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• 2015

본 논문에서는 학교 교육환경에서 청소년들에게 발생 할 수 있는소 셜미디어의 역기능을 빅 데이터 처리를 통하여 분석 할 수 있는 방법을 제시하고, 특히 악성 댓글을 위주로 한 청소년들 간의 소셜미디어를 중심으로 빅 데이터의 마이닝 기술을 활용하여 대표적인 청소년 문제의 확산을 방지 할 수 있는 시스템 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.676-691
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• 2022

An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.157-161
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• 2022

The coffee industry continues to grow steadily due to the spread of coffee and changes in consumer awareness. Once upon a time, instant coffee was common, People today have distinct personal preferences As consumption needs for favorite foods are segmented, ways to enjoy coffee are diversifying. This study was conducted through analysis of consumption changes for specialty coffee as a changed issue of COVID-19 The goal is to present a vision for the future of the specialty coffee industry. As a research method, text mining through big data analysis was conducted to extract and analyze factors affecting the change in specialty coffee consumption. As a result of the study, we judged that specialty coffee is consumed by using a drip tool that allows you to easily enjoy coffee at home after Corona 19. Therefore, hand drips used in home cafes were found to play a central role in the change in specialty coffee consumption.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.