• 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.

• The Journal of Bigdata
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• v.1 no.2
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• pp.91-102
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• 2016

In traditional environments we have called the data life cycle DIKW, which represents data-information-knowledge-wisdom. In big data environments, on the other hand, we call it DIA, which represents data-insight-action. The difference between the two data life cycles results in new architecture of data resource management. In this paper, we study data resource management architecture for big data environments. Especially main components of the architecture are proposed in this paper.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.19-20
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• 2015

Since 2013, Seoul Metropolitan Government (SMG) has introduced big data initiatively in administration and put into practices in transportation, safety, welfare in order to overcome limited resources and conflicting interests. For establishing a new midnight bus service, SMG prepared optimized midnight bus routes by analyzing big data from mobile phone Call Data Record (CDR) through collaboration with a telecommunication company. Despite of limited budget and resources, newly identified routes can cover over 42% of the citizen with 9 routes and less than 1% of buses compare with day time operation. In addition to solve transportation problem, SMG utilizes big data to resolve location selection problem for choosing new facility locations such as life double cropping centers and senior citizen leisure centers. As results, SMG demonstrates big data as a good tool to make policies and to build smarter city by overcome space-time limitation of resources, mediation of conflicts, and maximizes benefit of the citizen.

• Journal of Smart Tourism
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• v.1 no.3
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• pp.3-5
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• 2021

The advancement of Information Communication Technology has provided tourism researchers with a golden opportunity to access big data, which plays a critical role in smart tourism. Recognizing the current issue, this paper discusses the evolution of the literature on tourism big data focusing on conceptual understanding of and types of big data, and insights from big data analytics. Indeed, this article provides important research agenda for future tourism researchers who would like to conduct academic research about big data and smart tourism.

• The Journal of Industrial Distribution & Business
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• v.13 no.11
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• pp.1-10
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• 2022

Purpose: This study measures the influence of facilitating conditions on employees' attitudes towards the adoption of big data analytics by selected medical aid organizations in Durban. In the health care sector, there are various sources of big data such as patients' medical records, medical examination results, and pharmacy prescriptions. Several organizations take the benefits of big data to improve their performance and productivity. Research design, data, and methodology: A survey research strategy was conducted on some selected medical aid organizations. A non-probability sampling and the purposive sampling technique were adopted in this study. The collected data was analysed using version 23 of Statistical Package for Social Science (SPSS) Results: the results show that the "facilitating conditions" have a positive influence on employees' attitudes in the adoption of big data analytics Conclusions: The findings of this study provide empirical and scientific contributions of the facilitating conditions issues regarding employee attitudes toward big data analytics adoption. The findings of this study will add to the body of knowledge in this field and raise awareness, which will spur further research, particularly in developing countries.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.51-58
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• 2016

In the area of hotel and tourism sector, various research are analyzed using big data. Big data is being generated by any digital devices around us all the times. All the digital process and social media exchange produces the big data. In this paper, we analyzed the de-identification method of big data to use the personal information of hotel guests. Through the analysis of these big data, hotel can provide differentiated and diverse services to hotel guests and can improve the service and support the marketing of hotels. If the hotel wants to use the information of the guest, the private data should be de-identified. There are several de-identification methods of personal information such as pseudonymisation, aggregation, data reduction, data suppression and data masking. Using the comparison of these methods, the pseudonymisation is discriminated to the suitable methods for the analysis of information for the hotel guest. Also, among the pseudonymisation methods, the t-closeness was analyzed to the secure and efficient method for the de-identification of personal information in hotel.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.15-23
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• 2015

This thesis proposes how data security has changed since the emergence of 'Big Data' in 2012 and the type of Integrated Security Management System that needs to be built against security threats, based on an analysis of Big Data. Much research has been conducted in Big Data. I need to think about what an Integrated Security Management System requires in order to safeguard against security threats such as APT. I would like to draw a comparison between the current Integrated Security Management System and one that is based on Big Data, including its limitations and improvements, so that I can suggest a much improved version of Integrated Security Management System.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.13-23
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• 2016

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization's current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.

• Information Systems Review
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• v.18 no.2
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• pp.39-57
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• 2016

Previous studies on the success factors of big data implementation have called for future research and further examination of the top management leadership's impact. This research proposes and empirically tests three hypotheses, including how top management leadership can directly affect big data investment, how it can mediate the causal relationship between big data investment and idea usefulness, and how it can mediate the relationship between idea usefulness and business utilization. Based on the data collected from 108 big data users in Korean companies, we determined that all three hypotheses are statistically significant. By shedding light on top management leadership and its characteristics, we can provide better suggestions on what needs to be done to ensure the success of big data.