• Spatial Information Research
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• v.23 no.6
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• pp.109-116
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• 2015

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.85-90
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• 2022

In this study, keywords from representative online portal sites such as NAVER, Google, and Youtube were collected based on text mining analysis technique using TEXTOM to check the changes in the restaurant industry before and after COVID-19. The collection keywords were selected as dining out, food service industry, and dining out culture. For the collected data, the top 30 words were derived, respectively, through the refinement process. In addition, comparative analysis was conducted by defining data from 2018 to 2019 before COVID-19, and from 2020 to 2021 after COVID-19. As a result, 8272 keywords before COVID-19 and 9654 keywords after COVID-19, a total of 17926 keywords, were derived. In order for the food service industry to develop after the COVID-19 pandemic, it is necessary to commercialize the recipes of restaurants to revitalize the distribution of home-use food products that replace home-cooked meals such as meal kits. Due to the social distancing caused by COVID-19, the dining out culture has changed and the trend has changed, and it has been confirmed that the consumption culture has changed to eating and delivering at home more safely than visiting restaurants. In addition, it has been confirmed that the consumption culture of existing consumers is changing to a trend of cooking at home rather than visiting restaurants.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.33-41
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• 2016

Big data management is becoming an increasingly important issue in both industry and academia of information science community today. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used to improve system reliability. In this paper, we propose a novel big data management architecture specialized for log data. The proposed architecture provides a scalable log management system that consists of client and server side modules for efficient handling of log data. To support large and simultaneous log data from multiple clients, we adopt the Hadoop infrastructure in the server-side file system for storing and managing log data efficiently. We implement the proposed architecture to support various client environments and validate the efficiency through measurement studies. The results show that the proposed architecture performs better than the existing logging architecture by 42.8% on average. All components of the proposed architecture are implemented based on open source software and the developed prototypes are now publicly available.

• Culinary science and hospitality research
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• v.24 no.2
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• pp.87-95
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• 2018

This study investigates the relationship between Big 5 personality factors of food service employees and turnover intention and identifies the moderating effects of satisfactory parent-child relationship. A total of 179 data were subjected to frequency analysis and regression analysis. Participants were instructed to take an on-line survey which is google survey from August to October in 2017. The results of this study demonstrated that Big-5 personality factors excepted openness to experience influence on turnover intention. Furthermore, the parent-child relationship affects the relationship between Big-5 personality factors and turnover intention. This study may contribute to new data to human resource management.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.235-242
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• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.

• Smart Media Journal
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• v.5 no.4
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• pp.124-130
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• 2016

Big data technologies are increasing the need for big data in many areas of the world. Recently, the health care industry has become increasingly aware of the importance of disease and health care services, as it has become increasingly immune to prevention and health care. To do this, we need a Big data system to collect and analyze the personal biometric data. In this paper, we design the biometric big data system using low cost wearable device. We collect basic biometric data, such as heart rate, step count and physical activity from Mi Band, and store the collected biometric data into MongoDB. Based on the results of this study, it is possible to build a big data system that can be used in actual medical environment by using Hadoop etc. and to use it in real medical service in connection with various wearable devices for medical information.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.25-29
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• 2018

As society advances and consumers' perception of food safety changes, the demand for the safety of refrigerated and distributed foods is spreading. In this paper, we build an ICT system that can collect the TTH data from the NFC tag, store it, analyze the food safety level using it. This service platform consists of the temperature monitoring NFC tag attached to individual packaged food, the smartphone with NFC sensor, and the monitoring server with a big data system. Consumers and administrators in this system can easily identify and predict the safety level of individual packaged foods in real time using the provided app on smartphones. In the field of food safety, the use of new technologies such as ICT can create new value by combining with existing food industry in addition to creating new market by new service.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1083-1091
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• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.