• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.115-117
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• 2017

R has become a popular analytics platform as it provides powerful analytic functions as well as visualizations. However, it has a weakness in which scalability is limited. As an alternative, the RHadoop package facilitates distributed processing of R programs under the Hadoop platform. This paper investigates usefulness of the RHadoop package when analyzing healthcare big data that is widely open in the internet space. To do this, this paper has compared analytic performances of R and RHadoop using the medical treatment records of year 2015 provided by National Health Insurance Service. The result shows that RHadoop effectively enhances processing performance of healthcare big data compared with R.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.187-195
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• 2019

Recently, it has become possible to collect, store, process, and analyze data generated in various fields by the development of the technology related to the big data. These big data technologies are used for clinical results analysis and the optimization of clinical trial design will reduce the costs associated with health care. Therefore, in this paper, we are going to analyze clinical results and present guidelines that can reduce the period and cost of clinical trials. First, we use Sqoop to collect clinical results data from relational databases and store in HDFS, and use Hive, a processing tool based on Hadoop, to process data. Finally we use R, a big data analysis tool that is widely used in various fields such as public sector or business, to analyze associations.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.681-693
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• 2022

The purpose of this study is to suggest a plan to utilize atypical data in the health care field by inferring standard medical terms related to the musculoskeletal system through keyword network analysis of medical records of patients hospitalized for musculoskeletal disorders. The analysis target was 145 summaries of discharge with musculoskeletal disorders from 2015 to 2019, and was analyzed using TEXTOM, a big data analysis solution developed by The IMC. The 177 musculoskeletal related terms derived through the primary and secondary refining processes were finally analyzed. As a result of the study, the frequent term was 'Metastasis', the clinical findings were 'Metastasis', the symptoms were 'Weakness', the diagnosis was 'Hepatitis', the treatment was 'Remove', and the body structure was 'Spine' in the analysis results for each medical terminology system. 'Oxycodone' was used the most. Based on these results, we would like to suggest implications for the analysis, utilization, and management of unstructured medical data.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.35 no.5
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• pp.13-15
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• 2017

• KSCI Review
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• v.21 no.2
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• pp.17-26
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• 2013

• Korea Multimedia Society
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• v.21 no.4
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• pp.21-32
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• 2017

• Magazine of the Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.27-34
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• 2021

• Korea Information Processing Society Review
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• v.28 no.2
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• pp.34-43
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• 2021

• Smart Media Journal
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• v.8 no.1
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• pp.9-18
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• 2019

In the era of the 4th industrial revolution, the big data industry is gathering attention for new business models in the public and private sectors by utilizing various information collected through the internet and mobile. However, although the big data integration and analysis are performed with de-identification techniques, there is still a risk that personal privacy can be exposed. Recently, there are many studies to invent effective methods to maintain the value of data without disclosing personal information. In this paper, a personal information protection system is investigated to boost big data utilization in industrial sectors, such as healthcare and agriculture. The criteria for evaluating the de-identification adequacy of personal information and the protection scope of personal information should be differently applied for each industry. In the field of personal sensitive information-oriented healthcare sector, the minimum value of k-anonymity should be set to 5 or more, which is the average value of other industrial sectors. In agricultural sector, it suggests the inclusion of companion dogs or farmland information as sensitive information. Also, it is desirable to apply the demonstration steps to each region-specific industry.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.267-271
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• 2013

Our society has two prospective properties because of IT technology. Firstly, it is accelerated a degree of convergence. And convergence regions are expanded. For example, smart healthcare region was created by IT technology and medical industry. The efforts to convergence will be continued. Because of these properties, A number of data are made in our life. Through many devices such as smart phone, camera, game machine, tablet pc, various data types are produced. In this paper, we described utilization of Big Data. And we analysed Big Data processing process.