• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.721-728
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• 2013

Big data is one of the most spotlighted technological trends in these days, enabling new methods to handle huge volume of complicated data for a broad range of applications. Real-time network traffic analysis essentially deals with big data, which is comprised of different types of log data from various sensors. To tackle this problem, in this paper, we devise a big data based platform, RENTAP, to detect and analyse malicious network traffic. Focused on military network environment such as closed network for C4I systems, leading big data based solutions are evaluated to verify which combination of the solutions is the best design for network traffic analysis platform. Based on the selected solutions, we provide detailed functional design of the suggested platform.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.176-185
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• 2018

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.287-294
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• 2022

This paper examines the change and its meaning of marketing strategy in business administration, which is changing along with the development of digital technology. Unlike conventional marketing, digital marketing is creating new relationships and making changes through a two-way approach rather than a one-way approach between producers and consumers. And these changes are creating new approaches not only in the problems between businesses and consumers, but also in the relationship between public institutions and citizens. In particular, the potential of platforms, which are emerging as important in digital management, is applied to public policies, and efforts are being made to establish marketing strategies for public institutions. One case of this was applied to the cultural contents industry and policy to examine specific measures and visions. The cultural big data platform is in line with digital management and continuously utilizes digital marketing strategies in the public domain, and aims to promote creative work as well as publicize it to citizens and workers in the cultural content industry. The synergy effect that will emerge from the combination of the cultural big data platform and digital management is expected to continue.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.57-64
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• 2024

The use of big data needs to be emphasized in policy formulation by public officials in order to improve the transparency of government policies and increase efficiency and reliability of government policies. 'Hye-Ahn', a government-wide big data platform was built with this goal, and the subscribers of 'Hye-Ahn' has grown significantly from 2,000 at the end of 2016 to 100,000 at August 2018. Additionally, the central and local governments are expanding their big data related budgets. In this study, we derived the costs and benefits of 'Hye-Ahn' and used them to conduct an economic feasibility analysis. As a result, even if only some quantitative benefits are considered without qualitative benefits, the net present value, the benefit/cost, and internal rate of return turned out to be 22,662 million won, 2.3213, and 41.8%, respectively. Since this is larger than the respective comparison criteria of 0 won, 1.0, and 5.0%, it can be seen that 'Hye-Ahn' has had economic feasibility. As noticed earlier, the number of analysis using 'Hye-Ahn' is increasing, so it is expected that the benefits will increase as time passes. Finally, the socioeconomic value gained when the results of analysis using 'Hye-Ahn' are used in policy is expected to be significant.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.181-188
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• 2020

The fisheries industry is rapidly shifting from a traditional fishery to aquaculture paradigm and it faces various problems such as depletion of fishery resources and aging of fishing villages. We need the establishment of a fisheries big data platform that includes both the data of the central and surrounding industries of the fisheries industry for enhancement of establishment of a fisheries, 6th industrialization of fishing villages, establishment of related technical standards, and discovery of the new industries to overcome this. Data center agencies should collect, link, and pre-processing, and the platform organizer should create a water industry data virtuous circle through the establishment, operation, and data market of big data platforms to help overcome the current crisis, secure smart fisheries hegemony, and use it as a key to value transfer. Through this study, I would like to propose a policy and technical big data platform construction plan to successfully promote it.

• The Journal of Information Systems
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• v.29 no.1
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• pp.225-240
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• 2020

Purpose The purpose of this study is to identify features of Gapjil and platform tyranny through South Korea's Gapjil and platform tyranny cases and to suggest countermeasures to both kinds of cases and follow-up study subjects. Methodology/approach We examined South Korea's Gapjil and platform tyranny cases by using Big Data analytics. Then we made a close examination of the two typical cases, through which we compared features and countermeasures of Gapjil and those of platform tyranny. Findings Gapjil mostly occurred at conventional companies and franchise companies, between major and minor companies, or due to lack of owner's qualifications. The features of platform tyranny were excessively monopolistic structure of platform business, inadequate legal sanctions, and features of ICT companies. Establishment of legal bases for sanctions and education for platform participants were suggested as countermeasures.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.31-34
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• 2018

We live in the digital era, and the characteristics of our customers in the digital era are constantly changing. That's why understanding business requirements and converting them to technical requirements is essential, and you have to understand the data model behind the business layout. Moreover, BI(Business Intelligence) is at the crux of revolutionizing enterprise to minimize losses and maximize profits. In this paper, we have described a leading study about the situation of desk-top BI(software product & programming language) in aspect of front-end side and the Data Lake platform based on Big Data by data modeling in aspect of back-end side to support the business intelligence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2977-2997
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• 2018

In big data era, fresh data grows rapidly every day. More than 30,000 gigabytes of data are created every second and the rate is accelerating. Many organizations rely heavily on real time streaming, while big data stream computing helps them spot opportunities and risks from real time big data. Storm, one of the most common online stream computing platforms, has been used for big data stream computing, with response time ranging from milliseconds to sub-seconds. The performance of Storm plays a crucial role in different application scenarios, however, few studies were conducted to evaluate the performance of Storm. In this paper, we investigate the performance of Storm under different application scenarios. Our experimental results show that throughput and latency of Storm are greatly affected by the number of instances of each vertex in task topology, and the number of available resources in data center. The fault-tolerant mechanism of Storm works well in most big data stream computing environments. As a result, it is suggested that a dynamic topology, an elastic scheduling framework, and a memory based fault-tolerant mechanism are necessary for providing high throughput and low latency services on Storm platform.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.73-84
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• 2019

Machine learning as a service, the so-called MLaaS, has recently attracted much attention in almost all industries and research groups. The main reason for this is that you do not need network servers, storage, or even data scientists, except for the data itself, to build a productive service model. However, machine learning is often very difficult for most developers, especially in traditional science due to the lack of well-structured big data for scientific data. For experiment or application researchers, the results of an experiment are rarely shared with other researchers, so creating big data in specific research areas is also a big challenge. In this paper, we introduce the KISTI-ML platform, a community-based rapid AI model development for scientific data. It is a place where machine learning beginners use their own data to automatically generate code by providing a user-friendly online development environment. Users can share datasets and their Jupyter interactive notebooks among authorized community members, including know-how such as data preprocessing to extract features, hidden network design, and other engineering techniques.

• Journal of KIISE
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• v.42 no.4
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• pp.459-467
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• 2015

The analysis platform of remote-sensing big data is a system that downloads data from satellites, transforms it to a data type of L3, and then analyzes it and produces its analysis results. The objective of this paper is to develop ISO/IEC 9126-1 software quality model-based assessment criteria, in order to evaluate the quality of remote-sensing big data analysis platform. Its detailed research contents are as follows. First, the ISO/IEC 9216 standards and previous software evaluation models will be reviewed. Second, this paper will define evaluation areas, evaluation elements, and evaluation items for measuring the quality of big data analysis platform. Third, the validity of the assessment criteria will be verified by statistical experiments through content validity, reliability validity, and construct validity, by using SPSS 20.0 and Amos 20.0 software. The construct validity will also be conducted by performing the confirmatory factor analysis and path analysis. Lastly, it is significant that our research result demonstrates the first evaluation criteria in measuring the quality of big data analysis platform. It is also expected that our assessment criteria could be used as the basis information for evaluation criteria in the platforms that will be developed in the future.