• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.114-116
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• 2016

Today, the analysis of big data hae been used as an essential tool for finding customers' needs. Various big-data analysis sites have provided the analysis results with their own forms and styles according to their service and characteristics. Therefore, to use the analysis results for marketing fields, we have to understand the major characteristics on big data analysis tools. In this point, this study attempts to compare the characteristics of big data analysis results and styles from big data analysis sites.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.435-441
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• 2013

This paper review about kerberos security authentication scheme and policy for big data service. It analyzed problem for security technology based on Hadoop framework in big data service environment. Also when it consider applying problem of kerberos security authentication system, it analyzed deployment policy in center of main contents, which is occurred in commercial business. About the related applied Kerberos policy in US, it is researched about application such as cross platform interoperability support, automated Kerberos set up, integration issue, OPT authentication, SSO, ID, and so on.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.728-731
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• 2014

Today, the quality of medical service has become a major concern because that sustainable development of IT technology and extending people's life expectancy. This paper, it is used as a tool for the medical information quality management that analyze tweets big data form generated by individual's daily. The result of the analyze big data offers improvement medical information based evidence based medicine. Also it has been possible for a trace observation of chronic disease and can reduce additional other complications of patients. Therefore, effective treatment of disease and prevention is possible.

• 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.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.524-531
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• 2014

In this paper, we have proposed the processing and analyzing the linked open data (LOD), a kind of big-data, using resources of cloud computing. The LOD is web-based open data in order to share and recycle of public data. Specially, we defined the InfograaS (Info-graphic as a service), new business area of SaaS (software as a service), to support visualization technique for BA (business analytics) and Info-graphic. The goal of this study is easily to use it by the non-specialist and beginner without experts of visualization and business analysis. Data visualization is the process to represent visually and understand the data analysis easily. The purpose of data visualization is to deliver information clearly and effectively by chart and figure. The big data of public data are shared and presented in the charts and the graphics understood easily by various processing results using Hadoop, R, machine learning, and data mining of open source and resources of cloud computing.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.51-58
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• 2016

In the area of hotel and tourism sector, various research are analyzed using big data. Big data is being generated by any digital devices around us all the times. All the digital process and social media exchange produces the big data. In this paper, we analyzed the de-identification method of big data to use the personal information of hotel guests. Through the analysis of these big data, hotel can provide differentiated and diverse services to hotel guests and can improve the service and support the marketing of hotels. If the hotel wants to use the information of the guest, the private data should be de-identified. There are several de-identification methods of personal information such as pseudonymisation, aggregation, data reduction, data suppression and data masking. Using the comparison of these methods, the pseudonymisation is discriminated to the suitable methods for the analysis of information for the hotel guest. Also, among the pseudonymisation methods, the t-closeness was analyzed to the secure and efficient method for the de-identification of personal information in hotel.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1105-1115
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• 2015

Big data analysis for disaster have been recently started especially to text data such as social media. Social data usually supports for the final two stages of disaster management, which consists of four stages: prevention, preparation, response and recovery. Otherwise, big data analysis for meteorologic data can contribute to the prevention and preparation. This motivated us to review big data technologies dealing with non-text data rather than text in natural disaster area. To this end, we first explain the main keywords, big data, data mining and machine learning in sec. 2. Then we introduce the state-of-the-art machine learning techniques in meteorology-related field sec. 3. We show how the traditional machine learning techniques have been adapted for climatic data by taking into account the domain specificity. The application of these techniques in natural disaster response are then introduced (sec. 4), and we finally conclude with several future research directions.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.165-166
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• 2019

Big data analysis is the ability to collect, store, manage and analyze data from existing database management tools. Big data refers to large scale data that is generated in a digital environment, is large in size, has a short generation cycle, and includes not only numeric data but also text and image data. Big data is data that is difficult to manage and analyze in the conventional way. It has huge size, various types, fast generation and velocity. Therefore, companies in most industries are making efforts to create value through the application of Big data. In this study, we analyzed the meaning of keyword using Social Matrix, a big data analysis tool of Daum communications. Also, the theoretical implications are presented based on the analysis results.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.347-370
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• 2022

Most public library websites provide unstructured cultural program data, which cannot be produced and utilized systematically as bibliographic information. It is not sufficiently used in existing library big data research or cases, and there is a risk of disappearing when the website is reorganized or the person in charge is changed. This study developed a data schema that can be used in conjunction with bibliographic data by collecting and analyzing cultural programs and community service data produced in an unstructured manner and proposed to share and utilize public library cultural programs and community service data, and establish a library big data platform that can serve as an information channel between librarians who are cultural program planners. Library program data posted on the library website can be integrated and managed through the platform, securing continuity of work, and systematically managing and preserving the specialized service history of individual libraries.