• Title/Summary/Keyword: BIG4

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Healthcare service analysis using big data

  • Park, Arum;Song, Jaemin;Lee, Sae Bom
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.4
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    • pp.149-156
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    • 2020
  • In the Fourth Industrial Revolution, successful cases using big data in various industries are reported. This paper examines cases that successfully use big data in the medical industry to develop the service and draws implications in value that big data create. The related work introduces big data technology in the medical field and cases of eight innovative service in the big data service are explained. In the introduction, the overall structure of the study is mentioned by describing the background and direction of this study. In the literature study, we explain the definition and concept of big data, and the use of big data in the medical industry. Next, this study describes the several cases, such as technologies using national health information and personal genetic information for the study of diseases, personal health services using personal biometric information, use of medical data for efficiency of business processes, and medical big data for the development of new medicines. In the conclusion, we intend to provide direction for the academic and business implications of this study, as well as how the results of the study can help the domestic medical industry.

Comparison of Sentiment Analysis from Large Twitter Datasets by Naïve Bayes and Natural Language Processing Methods

  • Back, Bong-Hyun;Ha, Il-Kyu
    • Journal of information and communication convergence engineering
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    • v.17 no.4
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    • pp.239-245
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    • 2019
  • Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

Design and Development of Big Data Platform based on IoT-based Children's Play Pattern Analysis

  • Jung, Seon-Jin
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.4
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    • pp.218-225
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    • 2020
  • The purpose of this paper is to establish an IoT-based big data platform that can check the space and form analysis in various play cultures of children. Therefore, to this end, in order to understand the healthy play culture of children, we are going to build a big data platform that allows IoT and smart devices to work together to collect data. Therefore, the goal of this study is to develop a big data platform linked to IoT first in order to collect data related to observation of children's mobile movements. Using the developed big data platform, children's play culture can be checked anywhere through observation and intuitive UI design, quick information can be automatically collected and real-time feedback, data collected through repeaters can be aggregated and analyzed, and systematic database can be utilized in the form of big data.

Agriculture Big Data Analysis System Based on Korean Market Information

  • Chuluunsaikhan, Tserenpurev;Song, Jin-Hyun;Yoo, Kwan-Hee;Rah, Hyung-Chul;Nasridinov, Aziz
    • Journal of Multimedia Information System
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    • v.6 no.4
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    • pp.217-224
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    • 2019
  • As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

An Empirical Study on the Effects of Source Data Quality on the Usefulness and Utilization of Big Data Analytics Results (원천 데이터 품질이 빅데이터 분석결과의 유용성과 활용도에 미치는 영향)

  • Park, Sohyun;Lee, Kukhie;Lee, Ayeon
    • Journal of Information Technology Applications and Management
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    • v.24 no.4
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    • pp.197-214
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    • 2017
  • This study sheds light on the source data quality in big data systems. Previous studies about big data success have called for future research and further examination of the quality factors and the importance of source data. This study extracted the quality factors of source data from the user's viewpoint and empirically tested the effects of source data quality on the usefulness and utilization of big data analytics results. Based on the previous researches and focus group evaluation, four quality factors have been established such as accuracy, completeness, timeliness and consistency. After setting up 11 hypotheses on how the quality of the source data contributes to the usefulness, utilization, and ongoing use of the big data analytics results, e-mail survey was conducted at a level of independent department using big data in domestic firms. The results of the hypothetical review identified the characteristics and impact of the source data quality in the big data systems and drew some meaningful findings about big data characteristics.

A Study on the Effect of Organization's Environment on Acceptance Intention for Big Data System (빅데이터 시스템의 수용의도에 영향을 미치는 수용조직의 환경요인에 관한 연구)

  • Kim, Eun Young;Lee, Jung Hoon;Seo, Dong Ug
    • Journal of Information Technology Applications and Management
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    • v.20 no.4
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    • pp.1-18
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    • 2013
  • Big data has become a worldwide topic. Despite this, big data accurately understand and acquire the business to take advantage of companies that were only very few. The purpose of this study is to investigate the factors that effect Korean firm's adopting big data system. Empirical test was conducted to verify hypotheses using extended technology acceptance model and we analyzed factors which affect the behavioral intention of big data System. Based upon previous researches, we have selected organization innovation, organization slank, organization information system infra maturity, perceived benefits of big data system, perceived usefulness, perceived ease of use, behavioral intention as variables and proposed a research model based on survey questionnaires. From those, we drew that perceived usefulness and perceived ease of use influenced the behavioral intention. The results of this study will increase the users' awareness on big data system and contribute to develop a way to enable the introduction of new technologies.

Analysis of Social Welfare Effects of Onion Observation Using Big Data (빅데이터를 활용한 양파 관측의 사회적 후생효과 분석)

  • Joo, Jae-Chang;Moon, Ji-Hye
    • Korean Journal of Organic Agriculture
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    • v.29 no.3
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    • pp.317-332
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    • 2021
  • This study estimated the predictive onion yield through Stepwise regression of big data and weather variables by onion growing season. The economic feasibility of onion observations using big data was analyzed using estimated predictive data. The social welfare effect was estimated through the model of Harberger's triangle using onion yield prediction with big data and it without big data. Predicted yield using big data showed a deviation of -9.0% to 4.2%. As a result of estimating the social welfare effect, the average annual value was 23.3 billion won. The average annual value of social welfare effects if big data was not used was measured at 22.4 billion won. Therefore, it was estimated that the difference between the social welfare effect when the prediction using big data was used and when it was not was about 950 million won. When these results are applied to items other than onion items, the effect will be greater. It is judged that it can be used as basic data to prove the justification of the agricultural observation project. However, since the simple Harberger's triangle theory has the limitation of oversimplifying reality, it is necessary to evaluate the economic value through various methods such as measuring the effect of agricultural observation under a more realistic rational expectation hypothesis in future studies.

Characterizing Business Strategy in a New Ecosystem of Big Data (빅데이터 산업 활성화 전략 연구)

  • Yoo, Soonduck;Choi, Kwangdon;Shin, Sungyoung
    • Journal of Digital Convergence
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    • v.12 no.4
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    • pp.1-9
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    • 2014
  • This research describes strategies to promote the growth of the Big Data industry and the companies within the ecosystem. In doing so, we identify the roles and responsibilities of various objects of this ecosystem and Big Data concepts. We describe the five components of the Big Data ecosystem: governance, data holders, service users, service providers and infrastructure providers. Related to the Big Data industry, the paper discusses 13 business strategies between the five components in the ecosystem. These strategies directly respond to areas of research by the Big Data industry leading experts on its early development. These strategies focus on how companies can gain competitive advantages in a growing new business environment of Big Data. The strategy topics are as follows: 1) the government's long term policy, 2) building Big Data support centers, 3) policy support and improving the legal system, 4) improving the Privacy Act, 5) increasing the understanding of Big Data, 6) Big Data support excavation projects, 7) professional manpower education, 8) infrastructure system support, 9) data distribution and leverage support, 10) data quality management, 11) business support services development, 12) technology research and excavation, 13) strengthening the foundation of Big Data technology. Of the proposed strategies, establishing supportive government policies is essential to the successful growth of thee Big Data industry. This study fosters a better understanding of the Big Data ecosystem and its potential to increases the competitive advantage of companies.

A Study on the Development of the Key Promoting Talent in the 4th Industrial Revolution - Utilizing Six Sigma MBB competency-

  • Kim, Kang Hee;Ree, Sang bok
    • Journal of Korean Society for Quality Management
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    • v.45 no.4
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    • pp.677-696
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    • 2017
  • Purpose: This study suggests that Six Sigma MBB should be used as a key talent to lead the fourth industrial revolution era by training them with big data processing capability. Methods: Through the analysis between articles on the fourth industrial revolution and Six Sigma related papers, common competencies of data scientists and Six Sigma MBBs were identified and the big data analysis capabilities needed for Six Sigma MBB were derived. Then, training was conducted to improve the big data analysis capabilities so that Six Sigma MBB is able to design algorithms required in the fourth industrial revolution era. Results: Six Sigma MBBs, equipped with the knowledge in field site improvement and basic statistics, were provided with 40 hours of big data analysis training and then were made to design a big data algorithm. Positive results were obtained after applying a AI algorithm which could forecast process defects in a field site. Conclusion: Six Sigma MBB equipped with big data capability will make the best talent for the fourth industrial revolution era. A Six Sigma MBB has an excellent capability for improving field sites. Utilizing the competencies of MBB can be a key to success in the fourth industrial revolution. We hope that the results of this study will be shared with many companies and many more improved case studies will arise in the future as a result of this study.

A Study on the de-identification of Personal Information of Hotel Users (호텔 이용 고객의 개인정보 비식별화 방안에 관한 연구)

  • Kim, Taekyung
    • 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.