• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.137-144
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• 2018

The purpose of this study is to examine the effects of undergraduates' Big 5 personality on entrepreneurship. Particularly, this author has verified if immersion and self-efficacy have mediating effects in it. According to the results, first, undergraduates' Big 5 personality are found to exert effects on entrepreneurship. Second, there is difference found in the influence of Big 5 personality on self-efficacy. Third, Big 5 personality and a parameter, self-efficacy, have all significant influence on entrepreneurship. Also, in all the models, the parameter of self-efficacy has partly mediating effects in correlation between Big 5 personality and entrepreneurship. Based on these results, this researcher here suggests implications and provides suggestions for follow-up research.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Mycobiology
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• v.49 no.3
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• pp.280-284
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• 2021

Mushroom strains of Polyporales from the genera Coriolus, Trametes, Pycnoporus, Ganoderma, and Formitella were explored in terms of mycelial growth characteristics for the application of mushroom mycelia as alternative sources of materials replacing fossil fuel-based materials. Among the 64 strains of Polyporales, G. lucidum LBS5496GL was selected as the best candidate because it showed fast mycelial growth with high mycelial strength in both the sawdust-based solid medium and the potato dextrose liquid plate medium. Some of the Polyporales in this study have shown good mycelial growth, however, they mostly formed mycelial mat of weak physical strength. The higher physical strength of mycelial mat by G. lucidum LBS5496GL was attributed to its thick hyphae with the diameter of 13 mm as revealed by scanning electron microscopic analysis whereas the hyphae of others exhibited less than 2 mm. Glycerol and skim milk supported the best mycelial growth of LBS5496GL as a carbon and a nitrogen source, respectively.

• Journal of Acupuncture Research
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• v.26 no.4
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• pp.19-27
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• 2009

Objectives : To investigate the improvement rate of muscle weakness caused by lumbar disc herniation, 17 patients was treated with oriental medicine. Methods : To evaluate weakness of big toe extensor muscles caused by lumbar disc herniation, we measured the active range of motion(ROM) of big toe extension and muscle grading of big toe extensor muscles. We also measured numerical rating scale(NRS), oswestry disability index(ODI) and compared them with the active ROM of big toe extension. Results : 1. Progressive weakness of big toe extensor muscles was observed in 2 patients. Weakness of big toe extensor muscles were improved in 13 patients(76%). 2. Changes in manual muscle testing(MMT) scores corresponded with improvement rate of big toe extension. 3. Numerical rating scale(NRS) and oswestry disability index(ODI) improvement rate did not correspond with improvement rate of big toe extension. Conclusions : Muscle weakness caused by lumbar disc herniation can be treated with oriental medicine and progressive muscle weakness must be observed constantly.

• The Journal of Bigdata
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• v.4 no.2
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• pp.93-103
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• 2019

In this paper, we introduce a big data platform and a metadata management technique for fishery science R & D information. The big data platform collects and integrates various types of fisheries science R & D information and suggests how to build it in the form of a data lake. In addition to existing data collected and accumulated in the field of fisheries science, we also propose to build a big data platform that supports diverse analysis by collecting unstructured big data such as satellite image data, research reports, and research data. Next, by collecting and managing metadata during data extraction, preprocessing and storage, systematic management of fisheries science big data is possible. By establishing metadata in a standard form along with the construction of a big data platform, it is meaningful to suggest a systematic and continuous big data management method throughout the data lifecycle such as data collection, storage, utilization and distribution.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.463-483
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• 2014

Data are dramatically increased and big data technology is spotlighted innovative technology among the latest information technologies. Organizations are interested in adoption of big data system to analyze various data format and to identify new business opportunity. The purpose of this study is to build a unified model for a system adoption through analysis of impact that affects behavioral intention and usage behavior of using big data. This study in addition to Technology-Organization-Environment (TOE), that is used the introduction of organizational studies, and Diffusion of Innovation (DOI) have implemented an extended unified model including the unified theory of acceptance and use of technology (UTAUT) that is usually used in personal level adoption study. The hypothesis was set up after implementing research model, and then got 411 effective survey data to target the member of organizations. As a result, all models (UTAUT, TOE, DOI) are affect to behavioral intention and usage behavior. It is verified that the suggested unified model was appropriate.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.196-203
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• 2020

In this study, we tried to explore the actual direction for the play culture by looking at the social perception of the change of play culture due to the Corona 19 using big data analysis. For this research, we used Textom, a website specializing in collecting big data, and collected 10,216 data using keywords of "Corona + Play," "Play Culture" and "Leisure" from January 19, 2020 to September 30, 2020, when the first confirmed case of Corona 19 occurred in Korea on various portal sites at home and abroad. The results of this paper showed that the social perception of the play culture in Corona 19 was 51.61%, not much different from the negative image of 48.15%. It is necessary to develop a play culture program that can identify people's various desires and emotions under the premise that situations similar to the current With Corona period and Corona19 can occur at any time, and find mental and physical stability and vitality in unstable situations. In addition, the results of this study can be used as basic data for the development of play culture policies or programs, with the significance that this study helped vitalize big data utilization research in the fields of play, leisure, and culture.

• 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.