• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.301-314
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• 2013

The existing computer environment, numerous mobile environments and the internet environment make human life easier through the development of IT technology. With the emergence of the mobile and internet environment, data is getting bigger rapidly. From this environment, we can take advantage of using those data as economic assets for organizations which make dreams come true for the emerging Big Data environment and Big Data security services. Nowadays, Big Data services are increasing. However, these Big Data services about Big Data security is insufficient at present. In terms of Big Data security the number of security by Big Data studies are increasing which creates value for Security by Big Data not Security for Big Data. Accordingly in this paper our research will show how security for Big Data can vitalize Big Data service for organizations. In details, this paper derives the priorities of the components of Big Data Information Security Service by AHP.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.100-112
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• 2016

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.42-49
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• 2021

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

• Journal of Information Technology Applications and Management
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• v.24 no.3
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• pp.93-105
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• 2017

Demands of cosmetics are affected not only by the well-known elements such as brand, price, and customer's consumption capacity, but also by some latent factors, for example, weather and air environment. Due to complexity and dynamic changes of the above factors, their influences can hardly be estimated in an accurate way by the traditional approaches such as survey and questionnaires. Through modeling and statistical analysis of big data, this article studied the impacts of weather condition and air quality on customer flow and sales of the cosmetics distributors in China, and found several hidden influencing factors. It provided a big-data based method for the analysis of unconventional factors on cosmetics marketing in the changing weather condition and air environment.

• Convergence Security Journal
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• v.13 no.4
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• pp.69-75
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• 2013

Customer is the base factor of income for some corporations, so that effective CRM (Customer Relationship Management) is very important to develop the business. In order to use CRM efficiently, we should figure out customers' demands and provide services or products that the customers want. However, it is getting difficult to comprehend customers' demands because they have complicated form and getting more diverse. Recently, social media like Twitter and Facebook let customers to express their demands, and using big data is a very effective method for efficient CRM. This research suggests how to utilize big data for hotel CRM, which considers customer itself as asset of business. In addition, we discuss security problems of big data service and propose the solution for that.

• Journal of the Korean Society of Food Culture
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• v.38 no.2
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• pp.73-82
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• 2023

Since delivery food has become a new dietary culture, this study examines consumer awareness through big data analysis. We present the direction of delivery food for healthy eating culture and identify the current state of consumer awareness. Resources for big data analysis were mainly articles written by consumers on various websites; the collection period was divided into before and after COVID-19. Results of the big data analysis revealed that before COVID-19, delivery food was recognized as a limited product as a meal concept, but after COVID-19, it was recognized as a new shopping list and a new product for home parties. This study concludes by suggesting a new direction for healthy eating culture.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.15-22
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• 2020

In order to strengthen the disaster prevention phase and the management of social disasters, we will examine the plan of To-Be disaster management system interconnected by using intelligent information technologies such as IoT, Cloud, Big Data, Mobile and AI. The disaster management system can be upgraded by constructing an intelligent infrastructure based on Big Data analysis of the disaster signals before and after the disasters generated by private mobile and IoT. Big Data of disaster Signals can be customized to users in a timely manner through AI methodologies of supervised and unsupervised learning and reinforcement training. In the long term, it is expected that not only will the capacity of disaster response be improved, but the management ability centering on prevention will be enhanced as well.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.143-149
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• 2021

As the need to establish a big data system for rapid provision of big data and efficient management of resources has emerged due to rapid entry into the hyper-connected intelligence information society, public institutions are pushing to establish a big data system. Therefore, this study analyzed and combined the success factors of big data-related studies and the specific aspects of big data in public institutions based on the measurement of environmental factors for establishing an integrated information system for higher education institutions. In addition, 19 measurement items reflecting big data characteristics were derived from big data experts using brainstorming and Delphi methods, and a plan to successfully apply them to public institutions that want to build big data systems was proposed. We hope that this research results will be used as a foundation for the successful establishment of big data systems in public institutions.