• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.579-586
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• 2021

This study examines the application of big data and tasks of tourism based on the case of Jeju Special Self-Governing Province, which used big data for regional tourism policy. Through the use of big data, it is possible to understand rapidly changing tourism trends and trends in the tourism industry in a timely and detailed manner. and also could be used to elaborate existing tourism statistics. In addition, beyond the level of big data analysis to understand tourism phenomena, its scope has expanded to provide a platform for providing real-time customized services. This was made possible by the cooperative governance of industry, government, and academia for data building, analysis, infrastructure, and utilization. As a task, the limitation of budget dependence and institutional problems such as the infrastructure for building personal-level data for personalized services, which are the ultimate goal of smart tourism, and the Personal Information Protection Act remain. In addition, expertise and technical limitations for data analysis and data linkage remain.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.167-178
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• 2017

Big Data is a key asset for the company and a key factor in boosting its competitiveness in the logistics sector. However, there is still a lack of research on how to collect, analyze and utilize Big Data in logistics. In this context, this study has developed a value model applicable to logistics companies based on the results of analysis and application of Big Data in the logistics of previous studies and DHL. The purpose of this study is to improve the operational efficiency and customer experience maximization level of logistics companies through utilization of big data analysis in logistics, to improve competitiveness of big data utilization and to develop new business opportunities. This study has a significance to newly create a value model for utilization of big data analysis in logistics sector and can provide implications for other industries as well as logistics sector in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.91-112
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• 2019

The IoT environment has led to explosive growth of existing enterprise data, and how to utilize such big data is becoming an important issue in the management field. In this paper, major factors affecting the decisions of companies to utilize big data have been studied. And also, the effect of big data utilization on the management quality is studied empirically. During this process, we have studied the difference according to the award of Korean national quality award. As a result of the study, we confirmed that the five factors such as cost from technology, organization and environment perspective, compatibility, company size, chief officer support, and competitor pressure are key factors influencing big data utilization. Also, it was confirmed that the use of big data for management activities has an important influence on the six management quality factors based on MBNQA, and that the management quality level of Korean national quality award companies is relatively high. This paper provides practical implications for companies' use of big data because it demonstrates for the first time that big data utilization has an impact on management quality improvement.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1798-1806
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• 2020

In this study, we investigated whether there is a significant relationship between the natural radiation dose rate and the cancer incidences in Korea by using a big data analysis. The natural dose rate data for this analysis were the measurement data obtained from the 171 monitoring posts of the 113 administrative districts in Korea over the 10 years from 2007 to 2016. The relative cancer incidences for this analysis were the difference in the cancer patients per hundred thousand people year-on-year in the administrative districts with the five highest and the five lowest natural gamma dose rates each year over the same period. To analyze the correlation between the two variables, Spearman's rank correlation coefficient between the two rates was derived using R, a well-known big data analysis tool. The analysis showed that Spearman's rank correlation coefficient was more than 0.05 and that the correlation between the two variables was not statistically significant.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.422-424
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• 2018

Recently there has been growing interest in big data analysis and methods for collecting data have been developed diversely but researchers are still not easy to collect and use these large scale data. In this paper, researchers try to compare and analyze the method of collecting big data by using several methods and present it. I hope that you can provide the results of your research if you select and use methods that match your research objectives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.117-119
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• 2016

Various sites which provide big data analysis service do not show the interpretation of analysis results such as social trends and events but simple numeric results. In this point, this study attempts to suggest a way of interpretation on big data analysis results focusing on brand awareness and preference.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.1-7
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• 2023

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.