• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.87-95
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• 2022

Recently, with the development of smart technology, in medical information platform, patient's biometric data is measured in real time and accumulated into database, and it is possible to determine the patient's emergency situations. Medical staff can easily access patient information after simple authentication using a mobile terminal. However, in accessing medical information using the mobile terminal, it is necessary to study authentication in consideration of the patient situations and mobile terminal. In this paper, we studied on medical information platforms based on big data processing and edge computing for supporting automatic authentication in emergency situations. The automatic authentication system that we had studied is an authentication system that simultaneously performs user authentication and mobile terminal authentication in emergency situations, and grants upper-level access rights to certified medical staff and mobile terminal. Big data processing and analysis techniques were applied to the proposed platform in order to determine emergency situations in consideration of patient conditions such as high blood pressure and diabetes. To quickly determine the patient's emergency situations, edge computing was placed in front of the medical information server so that the edge computing determine patient's situations instead of the medical information server. The medical information server derived emergency situation decision values using the input patient's information and accumulated biometric data, and transmit them to the edge computing to determine patient-customized emergency situation. In conclusion, the proposed medical information platform considers the patient's conditions and determine quick emergency situations through big data processing and edge computing, and enables rapid authentication in emergency situations through automatic authentication, and protects patient's information by granting access rights according to the patient situations and the role of the medical staff.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.800-802
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• 2016

In these days, the profitability of cadastral survey for national spatial information is getting worse. In order to reinforce the structure of the profitability, there exists the necessity to launch new and various businesses except the cadastral survey. In manipulating national spatial data effectively, it is necessary to design a platform for spatial information. Against this backdrop, we propose a platform for spatial data on the basis of Big Data in this paper.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.65-72
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• 2020

Big data analysis is widely used in applications such as finance and communication, whose market size is growing rapidly every year. Nevertheless, it is rarely used by SMEs (small and medium-sized enterprises) since the existing services are not fully customized for them while being offered at high price. To resolve this, we develop and propose a new platform to provide big data analysis services specialized for SMEs in this paper. First, we compare existing work discussing social big data analysis, and extract service features necessary to help their marketing effectively. Then, we present a prototype system implementing the extracted features, and discuss technical issues needed to develop a complete system which are obtained from the prototype implementation.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• The Journal of Bigdata
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• v.5 no.1
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• pp.97-108
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• 2020

As the domestic electric power industry becomes a smart grid, big data platforms for demand management, facility management, and customer service have been deployed. However, due to the nature of the big data project, big data platforms take time to realize their value in the business processes. Therefore, it is not easy to evaluate the performance of the initial big data platforms using the known or theoretical evaluation methods. In this paper, we propose a methodology of big data platform performance evaluation based on specific information quality such as information completeness/sufficiency, information reliability, information relevancy, information comparability, information unbiasedness, timeliness of information, related to the volume, diversity, and velocity of big data.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.102-108
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• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.475-486
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• 2015

Mining the microarray data accumulated in the public data repositories can save experimental cost and time and provide valuable biomedical information. Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study. However, integrating several data sets from different studies is needed to deal with many problems. In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method based on rank. This method is compared with the diagonal linear discriminant analysis, k nearest neighbor method and support vector machine using the cross platform real example data sets of two cancers.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1593-1601
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• 2017

In a big data platform, association rule mining applications could bring some benefits. For instance, in a agricultural big data platform, the association rule mining application could recommend specific products for farmers to grow, which could increase income. The key process of the association rule mining is the frequent itemsets mining, which finds sets of products accompanying together frequently. Former researches about this issue, e.g. Apriori, are not satisfying enough because huge possible sets can cause memory to be overloaded. In order to deal with it, SON algorithm has been proposed, which divides the considered set into many smaller ones and handles them sequently. But in a single machine, SON algorithm cause heavy time consuming. In this paper, we present a method to find association rules in our Hadoop based big data platform, by parallelling SON algorithm. The entire process of association rule mining including pre-processing, SON algorithm based frequent itemset mining, and association rule finding is implemented on Hadoop based big data platform. Through the experiment with real dataset, it is conformed that the proposed method outperforms a brute force method.