• 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.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.73-99
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• 2014

Recently, big data have been studied ways to use in various fields. Big data refers to huge amounts of data that could not be addressed by conventional methods. Big data has attracted attention for improving accuracy of decision-making, forecasting in the near future, and creation of new business. In this study, it is an object to develop the utilization plan for big data in the field of technology commercialization. For this reason, we conducted study like case studies, literature review and focus group interview. We have derived the big data utilization plan based on this in the technology commercialization field. It, the data utilization plan, combines with the technology commercialization process of Jolly and it has five sub processes (Imagining, Incubating, Demonstrating, Promoting, Sustaining). In this paper, there is a significance that has emphasized the possibility for big data utilization in the technology commercialization. However, there is a limit to the general interpretation for our study. And we hope to contribute to the expansion of areas of technology commercialization information analysis through this research.

• The Korean Journal of Archival Studies
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• no.70
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• pp.85-117
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• 2021

The purpose of this study is to investigate and analyze the current status of unit tasks, unit task operation, and record management problems used by local governments, and to present improvement measures using text-based big data technology based on the implications derived from the process. Local governments are in a serious state of record management operation due to errors in preservation period due to misclassification of unit tasks, inability to identify types of overcommon and institutional affairs, errors in unit tasks, errors in name, referenceable standards, and tools. However, the number of unit tasks is about 720,000, which cannot be effectively controlled due to excessive quantities, and thus strict and controllable tools and standards are needed. In order to solve these problems, this study developed a system that applies text-based analysis tools such as corpus and tokenization technology during big data analysis, and applied them to the names and construction terms constituting the record management standard. These unit task operation support tools are expected to contribute significantly to record management tasks as they can support standard operability such as uniform preservation period, identification of delegated office records, control of duplicate and similar unit task creation, and common tasks. Therefore, if the big data analysis methodology can be linked to BRM and RMS in the future, it is expected that the quality of the record management standard work will increase.

• The Journal of Bigdata
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• v.2 no.1
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• pp.59-69
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• 2017

According to precedent research of disaster economics, most of the studies are either based on belated macroeconomic indicators or are limited to specific industries. It is certain that preventing disaster is important, but immediate analysis and reconstruction policy are crucial as well. This research analyzed the ripple effect of consumer spending followed by April 16 ferry disaster and MERS outbreak; it was done by applying credit card company's real-time big data with Marketing Mix Modeling. The main focus of this research is to see if it is possible to predict the scale of damage during ongoing disasters. It is found that setting up weekly MMM and moving the timeline draws significance conclusion. When disasters or events occur in future, this research may be the basis of building quick and intuitive indicator to monitor possible effects.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.89-114
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• 2021

This study analyzed the previous studies and utilization cases on library big data, and based on this, analyzed the collection/circulation data of the library big data platform and tried to derive meaningful analysis results. And five analysis indicators were selected: the increase rate of collections by annual, the composition of collections by subject, the composition of unborrowed collections by subject, the rate of borrowed collections, and use factor by subject. The analysis data is 6,722,603 cases of collection/circulation data from 33 public libraries in Busan. The main analysis results are as follows. First, it was found that the gap in the number of circulation was larger than the number of collection in the 33 public libraries. Second, the annual increase rate of collections also showed a clear decline. Third, each library showed a similar pattern in the composition of both the collections and the unborrowed collections by subject. Fourth, it was found that users' circulation were very different by subject and library. Fifth, in most libraries, the rate of circulation of collections and use factor in the natural science field were the highest.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.287-294
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• 2022

The purpose of this study is to find out consumers' perceptions of "metaverse," a newly spotlighted technology, through big data analysis as a non-face-to-face society continues after the outbreak of COVID-19. This study conducted a big data analysis using text mining to analyze consumers' perceptions of metaverse before and after COVID-19. The top 30 keywords were extracted through word purification, and visualization was performed through network analysis and concor analysis between each keyword based on this. As a result of the analysis, it was confirmed that the non-face-to-face society continued and metaverse emerged as a trend. Previously, metaverse was focused on textual data such as SNS as a part of life logging, but after that, it began to pay attention to virtual reality space, creating many platforms and expanding industries. The limitation of this study is that since data was collected through the search frequency of portal sites, anonymity was guaranteed, so demographic characteristics were not reflected when data was collected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.408-414
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• 2018

Various studies have been conducted to reduce marine accidents. However, research on marine incidents is only marginal. There are many reports of marine incidents, but the main content of existing studies has been qualitative, which makes quantitative analysis difficult. However, quantitative analysis of marine accidents is necessary to reduce marine incidents. The purpose of this paper is to analyze marine incident data quantitatively by applying big data techniques to predict marine incident trends and reduce marine accident. To accomplish this, about 10,000 marine incident reports were prepared in a unified format through pre-processing. Using this preprocessed data, we first derived major keywords for the Marine incidents at sea using text mining techniques. Secondly, time series and cluster analysis were applied to major keywords. Trends for possible marine incidents were predicted. The results confirmed that it is possible to use quantified data and statistical analysis to address this topic. Also, we have confirmed that it is possible to provide information on preventive measures by grasping objective tendencies for marine incidents that may occur in the future through big data techniques.

• Journal of Radiation Industry
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• v.11 no.1
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• pp.7-11
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• 2017

RI-Biomics is a fusion technology in radiation fields for evaluating in-vivo dynamics such as absorption, distribution, metabolism and excretion (RI-ADME) of new drugs and materials using radioisotopes and quantitative evaluation of their efficacy. RI-Biomics information is being provided by RIBio-Info developed as information system for distributing its information and three requirements for improving RIBio-Info system have been derived through reviewing recent big data trends in this study. Three requirements are defined as resource, technology and manpower, and some reviews for applying big data in RIBio-In system are suggested. Fist, applicable external big data have to be obtained, second, some infrastructures for realizing applying big data to be expanded, and finally, data scientists able to analyze large scale of information to be trained. Therefore, an original technology driven to analyze for atypical and large scale of data can be created and this stated technology can contribute to obtain a basis to create a new value in RI-Biomics field.

• The Journal of Bigdata
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• v.2 no.2
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• pp.17-31
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• 2017

With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.887-892
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• 2023

Vessel traffic service(VTS) centers are equipped with RADAR, AIS(Automatic Identification System), weather sensors, and VHF(Very High Frequency). VTS operators use this equipment to observe the movement of ships operating in the VTS area and provide information. The VTS data generated by these various devices is highly valuable for analyzing maritime traffic situation. However, owing to a lack of compatibility between system manufacturers or policy issues, they are often not systematically managed. Therefore, we developed the VTS Bigdata Platform that could efficiently collect, store, and manage control data collected by the VTS, and this paper describes its design and implementation. A microservice architecture was applied to secure operational stability that was one of the important issues in the development of the platform. In addition, the performance of the platform could be improved by dualizing the storage for real-time navigation information. The implemented system was tested using real maritime data to check its performance, identify additional improvements, and consider its feasibility in a real VTS environment.