• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.292-299
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• 2021

A form of data mining software, based on manufacturing-based IIoT environment with the development of IT technologies are increasingly growing. However, it is difficult to evaluate the software quality in the same form as general software due to the characteristics of the software of a manufacturing company that has a large amount of data that needs to be carried out with big data and data mining. In addition, in a manufacturing-based environment where heterogeneous equipment and software are mixed, it is difficult to perform quality judgment on software used by applying existing quality characteristics. Therefore, in this paper, the characteristics of the manufacturing base are investigated, and a software quality evaluation model suitable for it is developed and evaluated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1083-1091
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• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.113-116
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• 2020

본 연구의 목적은 최근 비중이 급증하고 있는 국내 전자상거래 시장에서 농산물 판매 현황이 지역 날씨와 생육 환경 등 농산물 생산과 연관성이 높은 데이터와 어떤 관계가 있는지를 분석하는 것이다. 이를 위해 전라남도 농산물의 온라인 판매 현황을 분석하고, 전남 지역 날씨와 생육 환경에 관한 표준화된 데이터를 안정적으로 확보할 수 있도록 빅데이터 시스템을 구축하고자 한다. 본 연구의 결과는 지역 농업인의 농산물 생산과 유통 의사결정에 시사점을 제공하고 궁극적으로는 생산성과 수익성 향성에 기여할 것으로 기대된다.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.507-517
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• 2020

The present research intends to examine the relationship between the Big Five personality traits and leadership initiations among the marketing executives in Delhi NCR (INDIA), and seeks to uncover the predictors of leadership initiations within personality traits. The data are collected through online survey method using different social media platforms. A sample of 233 (male =136 and female =97) marketing executive's responses were included. The data collected with the help of self-reported Big Five model inventory and leadership initiation test. The collected data were analyzed statistically by using descriptive statistics, correlation. and stepwise multiple regression analysis. The results revealed that the age of respondents inversely correlated with leadership initiation. Neuroticism revealed significant inverse correlation with leadership initiation, whereas significant positive correlations were found between extraversion, conscientiousness, agreeableness, and leadership initiations, while openness to experience revealed insignificant positive correlation with leadership initiation. Extraversion and conscientiousness appeared as the most dominant personality traits among marketing executives, irrespective of gender, that positively influenced leadership initiation and appeared as the predictor of leadership initiation. In male executives extraversion and age emerged as the predictors of leadership behavior, while in female executives extraversion and openness to experience personality traits appeared as the predictors of leadership initiation.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1071-1078
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• 2014

Recently, information providing service using Big Data is being expanded. Big Data processing technology is actively being academic research to an important issue in the IT industry. In this paper, we analyze a skilled pattern of welder through Big Data analysis or extraction of welding based on R programming. We are going to reduce cost on welding work including weld quality, weld operation time by providing analyzed results non-skilled welder. Welding has a problem that should be invested long time to be a skilled welder. For solving these issues, we apply connection rules algorithms and regression method to much pattern variable for welding pattern analysis of skilled welder. We analyze a pattern of skilled welder according to variable of analyzed rules by analyzing top N rules. In this paper, we confirmed the pattern structure of power consumption rate and wire consumption length through experimental results of analyzed welding pattern analysis.

• Research in Plant Disease
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• v.26 no.3
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• pp.121-133
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• 2020

Climate change, increased extreme weather and climate events, and rapidly changing socio-economic environment threaten agriculture and thus food security of our society. Therefore, it is urgent to shift from conventional farming to smart agriculture using big data and artificial intelligence to secure sustainable growth. In order to efficiently manage plant diseases through smart agriculture, agricultural big data that can be utilized with various advanced technologies must be secured first. In this review, we will first learn about agrometeorological big data consisted of meteorological, environmental, and agricultural data that the plant pathology communities can contribute for smart plant disease management. We will then present each sequential components of the smart plant disease management, which are prediction, monitoring and diagnosis, control, prevention and risk management of plant diseases. This review will give us an appraisal of where we are at the moment, what has been prepared so far, what is lacking, and how to move forward for the preparation of smart plant disease management.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.385-396
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• 2015

The purpose of this study are in selecting a core service and drawing an analysis functions and service sector, based on contents of geo-spatial big data. For the study, the demand survey in the methodology has to be done by reviewing of preceding geo-spatial big data service. The survey has conducted by targeting on those experts in Industry-Academy-Research cooperation. From the survey, we could draw out requirements for the analysis function and the geo-spatial big data service sector. Also, order of priorities in service of four fields(Society, Environment, Economy, Humanities) has been utilized by a QFD(Quality Function Deployment). With the data, the first two priorities and required sectors for each field were selected for the analysis functions. From the result, we could suggest the core service model(plan), and also expect developments following each sectoral core service in the future.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.173-178
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• 2020

As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.