• Korean Journal of Construction Engineering and Management
• /
• v.23 no.2
• /
• pp.54-64
• /
• 2022

Since the interest in big data is growing exponentially, various types of research and development in the field of big data have been conducted in the construction industry. Among various application areas, cost estimating can be a topic where the use of big data provides positive benefits. In order for firms to make efficient use of big data for estimating tasks, they need to establish a strategy based on the multifaceted analysis of internal and external environments. The objective of the study is to develop and propose a strategy of the use of big data for construction management(CM) firms' cost estimating tasks based on the SWOT analysis. Through the combined efforts of literature review, questionnaire survey, interviews and the SWOT analysis, the study suggests that CM firms need to maintain the current level of the receptive culture for the use of big data and expand incrementally information resources. It also proposes that they need to reinforce the weak areas including big data experts and practice infrastructure for improving the big data-based cost estimating.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.1
• /
• pp.89-97
• /
• 2014

Recently, spread of Smartphones caused activation of mobile services, because of that Big Data such as clouding service able to proceed with large amount of data which are hard to collect, save, search and analyze. Many companies collected variety of private and personal information without users' agreement for their business strategy and marketing. This situation raised social issues. As companies use Big Data, numbers of damage cases are growing. In this Thesis, when Big Data process, methods of analyze and research of data are very important. This thesis will suggest that choices of security levels and algorithms are important for security of private informations. To use Big Data, it has to encrypt the personal data to emphasize the importance of security level and selection of algorithm. Thesis will also suggest that research of utilization of Big Data and protection of private informations and making guidelines for users are require for security of private information and activation of Big Data industries.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.8
• /
• pp.77-84
• /
• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• Journal of Korea Multimedia Society
• /
• v.22 no.9
• /
• pp.1132-1139
• /
• 2019

Big data is prominently used as a prediction method in achieving a goal, because it can analyze the regularities to predict future results from a vast amount of past data. Furthermore, big data has huge influence in very diverse academic fields. On such awareness, this study analyzed the regular effect of e-Commerce usefulness from the effects which expectations on big-data service affect the usage purpose of e-Commerce usefulness. This study categorized e-Commerce usefulness into quality recognition, service, and ease, and studied how each category works between the relationship of big-data service expectation and the use intention.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.149-156
• /
• 2020

In the Fourth Industrial Revolution, successful cases using big data in various industries are reported. This paper examines cases that successfully use big data in the medical industry to develop the service and draws implications in value that big data create. The related work introduces big data technology in the medical field and cases of eight innovative service in the big data service are explained. In the introduction, the overall structure of the study is mentioned by describing the background and direction of this study. In the literature study, we explain the definition and concept of big data, and the use of big data in the medical industry. Next, this study describes the several cases, such as technologies using national health information and personal genetic information for the study of diseases, personal health services using personal biometric information, use of medical data for efficiency of business processes, and medical big data for the development of new medicines. In the conclusion, we intend to provide direction for the academic and business implications of this study, as well as how the results of the study can help the domestic medical industry.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.102-108
• /
• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

• International journal of advanced smart convergence
• /
• v.11 no.4
• /
• pp.68-80
• /
• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.483-486
• /
• 2021

With the explosive increase in data, the 'big data era' has arrived, focusing on deriving new values and insights through data. With the development of data analysis technology, the importance of data analysis and utilization in the field of diagnosis and treatment as well as prevention is expanding, while the use of big data is emerging in the healthcare field. Moreover, as the three data-related laws (Personal Information Protection Act, Information and Communication Network Act, and Credit Information Act) were passed in January 2020, it became possible to use a wide range of big data through pseudonym information. However, the use of healthcare big data is still struggling due to various policies and regulations, inconsistent data quality, and the absence of specialized personnel. Therefore, in this study, examines the current state of use of big data in the healthcare field, and analyzes the challenges, overseas cases, plans, and expected effects for activation of healthcare big data.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.99-109
• /
• 2020

This paper proposes a platform construction plan for the use of spectrum big data, collects and analyzes the big data in the radio wave field, establishes a linkage plan, and presents a support system scheme for linking and using the spectrum and public sector big data. It presented a plan to build a big data platform in connection with the spectrum public sector. In a situation where there is a lack of a support system for systematic analysis and utilization of big data in the field of radio waves, by establishing a platform construction plan for the use of big data by radio-related industries, the preemptive response to realize the 4th Industrial Revolution and the status and state of the domestic radio field. The company intends to contribute to enhancing the convenience of users of the big data platform in the public sector by securing the innovation growth engine of the company and contributing to the fair competition of the radio wave industry and the improvement of service quality. In addition, it intends to contribute to raising the social awareness of the value of spectrum management data utilization and establishing a collaboration system that uses spectrum big data through joint use of the platform.

• The Journal of Information Systems
• /
• v.30 no.3
• /
• pp.159-175
• /
• 2021

Purpose The purpose of this study was to find the effect of the determinants on the Big data utilization in industry. The determinants of Big data utilization were deduced by reviewing theoretical background and discussions on Big data related researches. Research model and proposed hypothesis were constructed from TOE framework and UTAUT model. Design/methodology/approach The research was conducted to collect a sample data from the experts involved in the Big data projects in industry. In addition, interviews and online survey were performed to get sample data. Exploratory factor analysis was conducted to verify the grouping of these questionnaire items and confirmatory factor analysis was done to verify the validity and reliability of the measurement model. Finally, research hypothesis was verified and theoretical and practical implications were proposed for further studies. Findings The results show that the technical factor have a significant effect on the expectancy factor and the behavioral factor. The organizational factor have a significant effect on the behavioral factor. In addition, the expectancy factor was significant on the behavioral factor and the intention-to-use of Big data system.