• 미국학
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• 제34권1호
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• pp.181-202
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• 2011

The purpose of this paper is to examine Paul Auster's Leviathan according to Slavoj Žižek's theory. Analyzing the characters in Leviathan, this paper chiefly discusses the postmodern subject's anxiety and obsessive repetition that the lack of the big Other led to. Section II explains the disintegration of the big Other and the subject's anxiety and obsessive repetition by the interpretation of the characters: Peter Aaron, Maria Turner, and Benjamin Sachs. Aaron wants to write on Sachs's life to overcome his uneasy subject's condition, and to establish the consistent and whole world. But his writing fails to meet his desire, owing to uncertainty of his understanding, and the incompleteness of his writing. In case of Maria, her uneasy subject's condition led to her obsessively repetitive picture-shooting herself and others, which proved to be a meaningless struggle for filling the void of the big Other and herself. Although Sachs already knows the lack and inconsistency of the big Other, he also repetitively tries to establish the consistent and whole Other. In Section III, this paper examines Sachs's terror as he struggles for the preservation of the big Other. His extreme striving also fails to reestablish the big Other as it loses its symbolic effectiveness in the postmodern era because he does not grasp the big Other as an empty Symbolic order, and rejects the premise of the big Other itself.

• 아태비즈니스연구
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• 제15권1호
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• pp.131-143
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• 2024

Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국BIM학회 논문집
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• 제6권3호
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• pp.24-33
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• 2016

Planning occurs from day-to-day, small-scale decisions to large-scale infrastructure investment decisions. For that reason, various attempts have been made to appropriately assist decision-making process and its optimization. Lately, initiation of a large amount of data, also known as big data has received great attention from diverse disciplines because of versatility and adoptability in its use and possibility to generate new information. Accordingly, implementation of big data and other information management systems, such as geographic information systems (GIS) and building information modeling (BIM) have received enough attention to establish each of its own profession and other associated activities. In this extent, this study illustrates a series of big data implementation cases that can provide a lesson to urban planning domain. In specific, case studies analyze how data was used to extract the most optimized solution and what aspects could be helpful in relation to planning decisions. Also, important notions about GIS and its application in various urban cases are examined.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 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.

• 디지털융복합연구
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• 제11권10호
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• pp.47-57
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• 2013

빅데이터 분석은 데이터의 양, 처리속도, 다양성 측면에서 데이터 마이닝과 달리 문제해결과 의사결정을 위해서는 새로운 도구를 필요로 한다. 많은 글로벌 IT기업들은 사용하기 쉽고 기능성이 우수한 모델링 능력을 가진 다양한 빅데이터 제품을 출시하고 있다. 빅데이터 패키지는 분석도구, 인프라, 플랫폼 형태로 하드웨어와 소프트웨어를 포함한 솔루션이다. 빅데이터의 수집, 저장, 분석, 시각화가 가능한 제품이다. 빅데이터 패키지는 업체별로 제품 종류가 많고 복잡한 기능을 가질 뿐만 아니라 선정에 있어서 전문 지식을 필요로 하며 일반적인 소프트웨어 패키지보다 그 중요성이 높기 때문에 의사결정 방법의 개발이 요구된다. 본 연구는 빅데이터 패키지 도입을 위한 의사결정지원방법을 제안하는 것이 목표이다. 문헌적 고찰을 통하여 빅데이터 패키지의 특징과 기능을 비교하고, 선정기준을 제안한다. 패키지 도입 타당성을 평가하기 위하여 비용과 혜택 각각을 목표노드로 하는 AHP 모델 및 선정기준을 목표노드로 하는 AHP 모델을 제안하고 이들을 결합하여 최적의 패키지를 선정하는 과정을 보인다.

• 한국유기농업학회지
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• 제29권3호
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• pp.317-332
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• 2021

This study estimated the predictive onion yield through Stepwise regression of big data and weather variables by onion growing season. The economic feasibility of onion observations using big data was analyzed using estimated predictive data. The social welfare effect was estimated through the model of Harberger's triangle using onion yield prediction with big data and it without big data. Predicted yield using big data showed a deviation of -9.0% to 4.2%. As a result of estimating the social welfare effect, the average annual value was 23.3 billion won. The average annual value of social welfare effects if big data was not used was measured at 22.4 billion won. Therefore, it was estimated that the difference between the social welfare effect when the prediction using big data was used and when it was not was about 950 million won. When these results are applied to items other than onion items, the effect will be greater. It is judged that it can be used as basic data to prove the justification of the agricultural observation project. However, since the simple Harberger's triangle theory has the limitation of oversimplifying reality, it is necessary to evaluate the economic value through various methods such as measuring the effect of agricultural observation under a more realistic rational expectation hypothesis in future studies.

• 한국산업융합학회 논문집
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• 제22권6호
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• pp.775-783
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• 2019

The big data industry market continues to grow and is expected to grow further. In this paper, based on the five-point Likert scale of college students in the process of developing big data young people, the satisfaction of instructors in big data training and improvement of job (education) ability based on AI convergence The survey was conducted on the expectations of the participants and their intention to participate in the training process for the young talents. Male students were more satisfied than students. In terms of students, students who are less than 6th semester have the highest satisfaction, but students who are less than 7th and 8th semesters are less satisfied. By department, the satisfaction level of science and statistics students was the highest, while the satisfaction level of other students was low. According to the average of college credits, the satisfaction of students under 3.5~4.0 was the highest, and the satisfaction of students below 3.0 was the lowest.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.188-190
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• 2015

현재의 빅데이터 환경은 비즈니스 목적뿐 아니라 다양한 목적을 위해 활용될 가능성이 있다. 이러한 점에서 본 연구에서는 빅데이터를 해 일반 대중들의 종교에 대한 인식 등을 분석하여, ICT 환경에서의 종교의 모습을 되돌아 보고, 나아가 종교적 측면에서의 ICT활용에 대한 방안을 제시하고자 한다.