• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.13-25
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• 2017

Apartments are a major type of residence and their number has continuously increased. Apartments have multiple meanings in that for public they are not only for residence purpose but for investment, a major commodity for construction firms and a critical policy measure of public well-fare for the government. Therefore, it is critical to understand and analyze trends in apartments demand for pro-active actions. The objective of the study is to analyze and identify key trends in apartments demand based on big data drawn from articles of major daily newspapers. The study identifies 17 major trends from seven themes including development, trade, sale in lots, location requirements, policy, residential environment, and investment and profit. The research methods in the study can be usefully applied to further studies for various issues in relation to the construction industry.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.25-37
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• 2022

The purpose of this study is to comprehensively check trends in policies, discourses, educational directions and contents, and social issues by deriving the subjective characteristics of peace education and unification education based on big data analysis. The results of this study are as follows. First, 'peace', 'unification', 'education', 'research', 'student', 'school', 'teacher', 'target', and 'Korean Peninsula' were commonly important keywords in peace education and unification education. Second, the top topic of peace education was 'peace education and civic education', and the top topic of unification education was ' sympathy and participation in unification education'. Third, topics that show an upward trend by regime in peace education were 'World Peace and Human Rights' and 'Object and Direction of Peace Education', and 'Subject of Unification Education' as topics that showed an upward trend by regime in unification education. Fourth, in peace education, the centrality of 'peace', 'education', 'student', 'school', and 'peace education' was high, and in unification education, 'unification', 'education', 'unification', 'school', and 'teacher' were high. Based on these results, it was intended to expand the horizon of understanding peace education and unification education, and to provide meaningful implications for establishing policies and conducting follow-up studies.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.142-151
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• 2020

The reason why today's public actively uses social communication is that the necessary information is collected and classified under the name of social big data through the web space to create the big data era, an ecosystem of information. In order for big data information to be used by the public, it is necessary to visualize it easily. This study categorized the types of visualization according to the information of social big data, and targeted the experienced students including the related majors and the general public who need to directly utilize and study the actual big data visualization as an experience evaluation target. As a result of analyzing the experiences of the experienced people, important implications for the visualization method for managing, analyzing, and utilizing the data were derived. The big data visualization strategy is to be expressed in a way that fits the data environment and user's eye level on SNS. In the future, if big data visualization is applied to product service or social trend, it will be an important data in terms of broadening its role, scope of application, and application.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.13-21
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• 2021

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.103-110
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• 2021

The purpose of this study is to check the trend of press articles and research trends in journal papers in the last 10 years, which dealt with the subject of 'university core competencies' with a big data analysis method. The main research methodology of this study applied the BigKinds analysis system and the semantic network analysis methodology. The results are as follows: First, the number of press articles related to university core competencies showed a keyword trend that rapidly increased in December 2014 and the second half of 2020. Related keywords were curriculum, specialization, project group, Ministry of Education, ACE, and competitiveness. Second, the semantic network value between keywords of related research papers showed 554 degree, 18,467 avg. degree, and 0.637 density. The degree of centrality of connection was analyzed in the order of university(1606), competency(1481), core(1349), and core competency(1301). Betweenness centrality was analyzed as core competencies(13.101), university students(13.101), university(13.101), and competencies(13.101). The results of this research are expected to give implications to future research and policy-making, educational program planning and operation, etc. to members of higher education institutions, experts in education policy, and educational scholars.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.2
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• pp.378-391
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• 2017

The application of Big Data has been introduced to the Korean fashion industry; however, the literature has not yet investigated how well high technologies are being perceived and adopted by the practitioners of fashion companies. Recognizing the lack of research, the current research explores how big data analysis has been adopted by fashion practitioners based on the Technology Acceptance Model (TAM) that considers the effect of organizational characteristics (i.e., innovation, slack, and IS infra maturity). First, all TAM relationships were accepted as significant; however, the effect of perceived ease of use on the attitude toward big data was greater than perceived usefulness. Regarding organizational characteristics, while organization innovation had positive impacts on perceived usefulness as well as perceived ease of use, organization slack did not show significant and positive influence on perceived ease of use only. On the other hand, IS infra maturity had a negative effect on perceived usefulness while it did not have any significant impact on perceived ease of use. Finally, the level of perceived usefulness is decreasing as the IS infra of the fashion organization becomes more mature. With the results, the study suggested that fashion industry needs more education on the usage of big data analysis systems and development in related analysis tools.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.100-112
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• 2016

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.997-1004
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• 2012

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.