• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.64-73
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• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Knowledge Management Research
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• v.21 no.4
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• pp.243-263
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• 2020

The purpose of this study is to propose the performance measurement model for open big data platform. In order to develop the performance measurement model, we have integrated big data reference architecture(NIST 2018) with performance prism model(Neely et al. 2001) in the platform perspective of open big data. Our proposed model consists of five key building blocks for measuring performance of open data platform as follows: stakeholder contribution, big data governance capabilities, big data service capabilities, big data IT capabilities, and stakeholder satisfaction. In addition, our proposed model have twenty four evaluation indices and seventy five measurement items. We believe that our model could offer both research and practical implications for relevant research.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.95-106
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• 2016

The major thrust of this research focuses on the development of phased big data distribution model based on the big data ecosystem. This model consists of 3 phases. In phase 1, data intermediaries are participated in this model and transaction functions are provided. This system consists of general control systems, registrations, and transaction management systems. In phase 2, trading support systems with data storage, analysis, supply, and customer relation management functions are designed. In phase 3, transaction support systems and linked big data distribution portal systems are developed. Recently, emerging new data distribution models and systems are evolving and substituting for past data management system using new technology and the processes in data science. The proposed model may be referred as criteria for industrial standard establishment for big data distribution and transaction models in the future.

• Journal of Distribution Science
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• v.22 no.8
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• pp.65-76
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• 2024

Purpose: The study aims to recognize the relationship between big data analytics capabilities, big data analytics process, and perceived business performance from supply chain management to total customer satisfaction. Research design, data and methodology: The study followed a quantitative approach with a descriptive design. The data was collected from leading e-commerce companies in India using a structured questionnaire, and the data was coded and decoded using MS Excel, SPSS, and R language. It was further tested using Cronbach's alpha, KMO, and Bartlett's test for reliability and internal consistency. Results: The results showed that the big data analytics process acts as a robust mediator between big data analytics capabilities and perceived business performance. The 'direct, indirect and total effect of the model' and 'PLS-SEM model' showed that the big data analytics process directly impacts business performance. Conclusions: A complete indirect relationship exists between big data analytics capabilities and perceived business performance through the big data analytics process. The research contributesto e-commerce companies' understanding of the importance of big data analytics capabilities and processes.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.1-18
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• 2013

Big data has become a worldwide topic. Despite this, big data accurately understand and acquire the business to take advantage of companies that were only very few. The purpose of this study is to investigate the factors that effect Korean firm's adopting big data system. Empirical test was conducted to verify hypotheses using extended technology acceptance model and we analyzed factors which affect the behavioral intention of big data System. Based upon previous researches, we have selected organization innovation, organization slank, organization information system infra maturity, perceived benefits of big data system, perceived usefulness, perceived ease of use, behavioral intention as variables and proposed a research model based on survey questionnaires. From those, we drew that perceived usefulness and perceived ease of use influenced the behavioral intention. The results of this study will increase the users' awareness on big data system and contribute to develop a way to enable the introduction of new technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2239-2256
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• 2022

In this paper, a model is proposed to comprehensively evaluate the computational power. The five forces model of computational power solves the problem that the measurement units of different indexes are not unified in the process of computational power evaluation. It combines the bidirectional projection method with TOPSIS method. This model is more scientific and effective in evaluating the comprehensive situation of computational power. Lastly, an example shows the validity and practicability of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.59-66
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• 2021

Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1562-1565
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• 2020

With the development of digital technology, the industrial structure is becoming digitalize. The government selected big data as the key technology of the 4th industrial revolution. Among them, big data is widely used to create new values and services by utilizing vast amounts of information. In order to cultivate professional manpower for the use of big data, various education programs are provided at universities. We intend to develop a curriculum for systematic training of talented people who can acquire knowledge about the three stages of collection, analysis, and application of big data. To this end, subjects are classified into basic competency, technical competency, analysis competency, and business competency based on the big data competency model proposed by the Korea Internet & Security Agency.