• The Journal of Bigdata
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• v.5 no.2
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• pp.171-186
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• 2020

The proportion of micro-enterprises and self-employed in Korea is excessively high compared to that of major developed countries, and frequent start-ups and business closures are repeated, causing enormous damage to the national economy. In order to solve this problem, various studies are underway for micro-enterprises, and the government provides commercial district information analysis services using big data for micro-enterprises. Among the commercial district information analysis services, the commercial district information analysis of our village store operated by the Seoul Metropolitan Government is continuously improving its service to provide the big data analysis service related to micro-enterprises. Since the service was built by integrating big data provided by various organizations, however, there are limitations in data reliability, data analysis, and service composition. In order to overcome these limitations, this paper proposes a location-based survey system that can be analyzed in conjunction with big data-based commercial district services. The proposed questionnaire survey system established the basis for expending the big data commercial district analysis service by linking the survey information and commercial district information.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.

• The Journal of Bigdata
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• v.1 no.1
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• pp.41-51
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• 2016

Big data is an emerging issue as large data which was impossible to be processed in the past is possible to be handled with the development of information and communication technology. Manufacturing is the most promising field that big data is applied such that there are abundant data available. It is important to improve an efficiency of manufacturing process for quality control and production efficiency because the processes from production design, sales, productions and so on are mixed intricately. This study proposes big data cloud service for manufacturing analysis using a big data technology and a process mining technique. It is expected for manufacturing corporations to improve a manufacturing process and reduced the cost by applying the proposed service. The service provides various analyses including manufacturing analysis and manufacturing duration analysis. Big data cloud service has been implemented and it has been validated by conducting a case study.

• The Journal of Bigdata
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• v.2 no.2
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• pp.87-93
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• 2017

The research on data quality has been performed for a long time. However, the research focused on structured data. With the recent digital revolution or the fourth industrial revolution, quality control of big data is becoming more important. In this paper, we analyze and classify big data quality types through previous research. The types of big data quality can be classified into value, data structure, process, value chain, and maturity model. Based on these comparative studies, this paper proposes a new standard, service quality of big data.

• Journal of Preventive Medicine and Public Health
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• v.50 no.5
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• pp.294-302
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• 2017

Objectives: The objectives of this study were to investigate the agreement between medical history questionnaire data and claims data and to identify the factors that were associated with discrepancies between these data types. Methods: Data from self-reported questionnaires that assessed an individual's history of hypertension, diabetes mellitus, dyslipidemia, stroke, heart disease, and pulmonary tuberculosis were collected from a general health screening database for 2014. Data for these diseases were collected from a healthcare utilization claims database between 2009 and 2014. Overall agreement, sensitivity, specificity, and kappa values were calculated. Multiple logistic regression analysis was performed to identify factors associated with discrepancies and was adjusted for age, gender, insurance type, insurance contribution, residential area, and comorbidities. Results: Agreement was highest between questionnaire data and claims data based on primary codes up to 1 year before the completion of self-reported questionnaires and was lowest for claims data based on primary and secondary codes up to 5 years before the completion of self-reported questionnaires. When comparing data based on primary codes up to 1 year before the completion of selfreported questionnaires, the overall agreement, sensitivity, specificity, and kappa values ranged from 93.2 to 98.8%, 26.2 to 84.3%, 95.7 to 99.6%, and 0.09 to 0.78, respectively. Agreement was excellent for hypertension and diabetes, fair to good for stroke and heart disease, and poor for pulmonary tuberculosis and dyslipidemia. Women, younger individuals, and employed individuals were most likely to under-report disease. Conclusions: Detailed patient characteristics that had an impact on information bias were identified through the differing levels of agreement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.385-396
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• 2015

The purpose of this study are in selecting a core service and drawing an analysis functions and service sector, based on contents of geo-spatial big data. For the study, the demand survey in the methodology has to be done by reviewing of preceding geo-spatial big data service. The survey has conducted by targeting on those experts in Industry-Academy-Research cooperation. From the survey, we could draw out requirements for the analysis function and the geo-spatial big data service sector. Also, order of priorities in service of four fields(Society, Environment, Economy, Humanities) has been utilized by a QFD(Quality Function Deployment). With the data, the first two priorities and required sectors for each field were selected for the analysis functions. From the result, we could suggest the core service model(plan), and also expect developments following each sectoral core service in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.207-215
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• 2016

The demand for spatial data service technologies is increasing lately with the development of realistic 3D spatial information services and ICT (Information and Communication Technology). Research is being conducted on the real-time provision of spatial data services through a variety of mobile and Web-based contents. Big data or cloud computing can be presented as alternatives to the construction of spatial data for the effective use of large volumes of data. In this paper, the process of building HLM (Hyper Live Map) using multi-source data to acquire stereo CCTV and other various data is presented and a big data service architecture design is proposed for the use of flexible and scalable cloud computing to handle big data created by users through such media as social network services and black boxes. The provision of spatial data services in real time using big data and cloud computing will enable us to implement navigation systems, vehicle augmented reality, real-time 3D spatial information, and single picture based positioning above the single GPS level using low-cost image-based position recognition technology in the future. Furthermore, Big Data and Cloud Computing are also used for data collection and provision in U-City and Smart-City environment as well, and the big data service architecture will provide users with information in real time.

• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• Spatial Information Research
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• v.22 no.6
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• pp.13-21
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• 2014

This study defines concept and service framework of Geo-Spatial Big Data(GSBD). The major concept of the GSBD is formulated based on the 7V characteristics: the general characteristics of big data with 3V(Volume, Variety, Velocity); Geo-spatial oriented characteristics with 4V(Veracity, Visualization, Versatile, Value). GSBD is the technology to extract meaningful information from Geo-spatial fusion data and support decision making responding with rapidly changing activities by analysing with almost realtime solutions while efficiently collecting, storing and managing structured, semi-structured or unstructured big data. The application area of the GSBD is segmented in terms of technical aspect(store, manage, analyze and service) and public/private area. The service framework for the GSBD composed of modules to manage, contain and monitor GSBD services is suggested. Such additional studies as building specific application service models and formulating service delivery strategies for the GSBD are required based on the services framework.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.139-151
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• 2023

This study aims to gain insights into ChatGPT, which has recently received significant attention. The study utilized a mixed method involving case studies and news big data analysis. ChatGPT can be described as an optimized language model for dialogue. The question arises whether ChatGPT will replace Google search services, posing a potential threat to Google. It could hurt Google's advertising business, which is the foundation of its profits. With AI-based chatbots like ChatGPT likely to disrupt the web search industry, Google is establishing a new AI strategy. The study used the BIG KINDS service and analyzed 2,136 articles over six months, from August 23, 2022, to February 22, 2023. Thirty of these articles were written in 2022, while 2,106 have been reported recently as of February 22, 2023. Also, the study examined the contents of ChatGPT by utilizing literature research, news big data analysis, and use cases. Despite limitations such as the potential for false information, analyzing news big data and use cases suggests that ChatGPT is worth using.