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A method of Securing Mass Storage for SQL Server by Sharing Network Disks - on the Amazon EC2 Windows Environments - (네트워크 디스크를 공유하여 SQL 서버의 대용량 스토리지 확보 방법 - Amazon EC2 Windows 환경에서 -)

  • Kang, Sungwook;Choi, Jungsun;Choi, Jaeyoung
    • Journal of Internet Computing and Services
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    • v.17 no.2
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    • pp.1-9
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    • 2016
  • Users are provided infrastructure such as CPU, memory, network, and storage as IaaS (Infrastructure as a Service) service on cloud computing environments. However storage instances cannot support the maximum storage capacity that SQL servers can use, because the capacity of instances provided by service providers is usually limited. In this paper, we propose a method of securing mass storage capacity for SQL servers by sharing network disks with limited storage capacity. We confirmed through experiments that it is possible to secure mass storage capacity, which exceeds the maximum storage capacity provided by an instance with Amazon EBS on Amazon EC2 Windows environments, and it is possible to improve the overall performance of the SQL servers by increasing the disk capacity and performance.

Carcass characteristics and meat quality of lambs that are fed diets with palm kernel cake

  • Santos, Rozilda da Conceicao dos;Gomes, Daiany Iris;Alves, Kaliandra Souza;Mezzomo, Rafael;Oliveira, Luis Rennan Sampaio;Cutrim, Darley Oliveira;Sacramento, Samara Bianca Moraes;Lima, Elizanne de Moura;Carvalho, Francisco Fernando Ramos de
    • Asian-Australasian Journal of Animal Sciences
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    • v.30 no.6
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    • pp.865-871
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    • 2017
  • Objective: The aim was to evaluate carcass characteristics, cut yield, and meat quality in lambs that were fed different inclusion levels of palm kernel cake. Methods: Forty-five woolless castrated male Santa $In{\hat{e}}s$ crossbred sheep with an initial average body weight of $23.16{\pm}0.35kg$ were used. The experimental design was a completely randomized design with five treatments, with palm kernel cake in the proportions of 0.0%, 7.5%, 15.0%, 22.5%, and 30.0% with nine replications per treatment. After slaughter, the gastrointestinal tract was weighed when it was full, after which it was then emptied. The heart, liver, kidney, pancreas perirenal fat were also collected and weighed. The carcass was split into two identical longitudinal halves and weighed to determine the quantitative and qualitative characteristics. Results: The empty body weight, carcass weight and yield, and fat thickness decreased linearly (p<0.05) as a function of palm kernel inclusion in the diet. There was no difference (p>0.05) for the rib eye area of animals that were fed palm kernel cake. There was a reduction in the commercial cut weight (p<0.05), except for the neck weight. The weights of the heart, liver, kidney fat, small, and large intestine, and gastrointestinal tract decreased. Nevertheless, the gastrointestinal content was greater for animals that were fed increasing levels of cake. For the other organs and viscera, differences were not verified (p>0.05). The sarcomere length decreased linearly (p<0.05), although an effect of the inclusion of palm kernel cake was not observed in other meat quality variables. It is worth noting that the red staining intensity, indicated as A, had a tendency to decrease (p = 0.050). Conclusion: The inclusion of palm kernel cake up to 30% in the diet does not lead to changes in meat quality characteristics, except for sarcomere length. Nevertheless, carcass quantitative characteristics decrease with the use of palm kernel cake.

Characteristics and Implications of Sports Content Business of Big Tech Platform Companies : Focusing on Amazon.com (빅테크 플랫폼 기업의 스포츠콘텐츠 사업의 특징과 시사점 : 아마존을 중심으로)

  • Shin, Jae-hyoo
    • Journal of Venture Innovation
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    • v.7 no.1
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    • pp.1-15
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    • 2024
  • This study aims to elucidate the characteristics of big tech platform companies' sports content business in an environment of rapid digital transformation. Specifically, this study examines the market structure of big tech platform companies with a focus on Amazon, revealing the role of sports content within this structure through an analysis of Amazon's sports marketing business and provides an outlook on the sports content business of big tech platform companies. Based on two-sided market platform business models, big tech platform companies incorporate sports content as a strategy to enhance the value of their platforms. Therefore, sports content is used as a tool to enhance the value of their platforms and to consolidate their monopoly position by maximizing profits by increasing the synergy of platform ecosystems such as infrastructure. Amazon acquires popular live sports broadcasting rights on a continental or national basis and supplies them to its platforms, which not only increases the number of new customers and purchasing effects, but also provides IT solution services to sports organizations and teams while planning and supplying various promotional contents, thus creates synergy across Amazon's platforms including its advertising business. Amazon also expands its business opportunities and increases its overall value by supplying live sports contents to Amazon Prime Video and Amazon Prime, providing technical services to various stakeholders through Amazon Web Services, and offering Amazon Marketing Cloud services for analyzing and predicting advertisers' advertising and marketing performance. This gives rise to a new paradigm in the sports marketing business in the digital era, stemming from the difference in market structure between big tech companies based on two-sided market platforms and legacy global companies based on one-sided markets. The core of this new model is a business through the development of various contents based on live sports streaming rights, and sports content marketing will become a major field of sports marketing along with traditional broadcasting rights and sponsorship. Big tech platform global companies such as Amazon, Apple, and Google have the potential to become new global sports marketing companies, and the current sports marketing and advertising companies, as well as teams and leagues, are facing both crises and opportunities.

Design and Forensic Analysis of a Zero Trust Model for Amazon S3 (Amazon S3 제로 트러스트 모델 설계 및 포렌식 분석)

  • Kyeong-Hyun Cho;Jae-Han Cho;Hyeon-Woo Lee;Jiyeon Kim
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.33 no.2
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    • pp.295-303
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    • 2023
  • As the cloud computing market grows, a variety of cloud services are now reliably delivered. Administrative agencies and public institutions of South Korea are transferring all their information systems to cloud systems. It is essential to develop security solutions in advance in order to safely operate cloud services, as protecting cloud services from misuse and malicious access by insiders and outsiders over the Internet is challenging. In this paper, we propose a zero trust model for cloud storage services that store sensitive data. We then verify the effectiveness of the proposed model by operating a cloud storage service. Memory, web, and network forensics are also performed to track access and usage of cloud users depending on the adoption of the zero trust model. As a cloud storage service, we use Amazon S3(Simple Storage Service) and deploy zero trust techniques such as access control lists and key management systems. In order to consider the different types of access to S3, furthermore, we generate service requests inside and outside AWS(Amazon Web Services) and then analyze the results of the zero trust techniques depending on the location of the service request.

A Causal Recommendation Model based on the Counterfactual Data Augmentation: Case of CausRec (반사실적 데이터 증강에 기반한 인과추천모델: CausRec사례)

  • Hee Seok Song
    • Journal of Information Technology Applications and Management
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    • v.30 no.4
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    • pp.29-38
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    • 2023
  • A single-learner model which integrates the user's positive and negative perceptions is proposed by augmenting counterfactual data to the interaction data between users and items, which are mainly used in collaborative filtering in this study. The proposed CausRec showed superior performance compared to the existing NCF model in terms of F1 value and AUC in experiments using three published datasets: MovieLens 100K, Amazon Gift Card, and Amazon Magazine. Compared to the existing NCF model, the F1 and AUC values of CausRec showed 1.2% and 2.6% performance improvement in MovieLens 100K data, and 2.2% and 10% improvement in Amazon Gift Card data, respectively. In particular, in experiments using Amazon Magazine data, F1 and AUC values were improved by 11.7% and 21.9%, respectively, showing a significant performance improvement effect. The performance of CausRec is improved because both positive and negative perceptions of the item were reflected in the recommendation at the same time. It is judged that the proposed method was able to improve the performance of the collaborative filtering because it can simultaneously alleviate the sparsity and imbalance problems of the interaction data.

The Development of Personal Computer Control System Using Voice Command (음성 명령을 이용한 개인용 컴퓨터 조작 시스템의 구현)

  • Lee, Tae Jun;Kim, Dong Hyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.10a
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    • pp.101-102
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    • 2018
  • Users who using computer may experience fatigue or sickness on their wrists if they use the keyboard and mouse for a long time. People with physical disabilities will find it difficult to work with the keyboard and mouse. There is a problem in that the substitute product for solving this is limited in function or expensive. In this paper, we development a system for controlling a personal computer with voice commands using the Amazon Echo and Amazon Web Services lambda functions. The implemented system processes the user's voice commands from the Amazon web server and sends them to the personal computer. The personal computer processes the received command and uses it to operate the application program.

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Leveraging Big Data for Spark Deep Learning to Predict Rating

  • Mishra, Monika;Kang, Mingoo;Woo, Jongwook
    • Journal of Internet Computing and Services
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    • v.21 no.6
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    • pp.33-39
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    • 2020
  • The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

A Study on the Architectural Changes of the Logistics Center due to Automation and Enlargement - Focusing on the case of Coupang, E-Mart, Amazon Logistics Centers - (자동화, 대형화로 인한 물류센터의 건축적 변화에 대한 고찰 - 쿠팡, 이마트, Amazon 물류센터의 사례를 중심으로 -)

  • Jo, Yong-Hyun;Choi, Choon
    • Journal of the Architectural Institute of Korea Planning & Design
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    • v.36 no.1
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    • pp.37-48
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    • 2020
  • Logistics centers, distribution centers, or warehouse facilities increasingly dominate urban and suburban landscapes, their enormous but blank, repetitive facades completely overshadowing adjacent buildings. Mostly ignored by architects, this new building type symbolically represents the arrival of post-anthropocene, or post-urbicene era of architecture, in which an increasing portion of our built environment will not be intended for human occupancy, but rather for use by machines and artificial intelligence. As a new wave of logistics centers are becoming more automated, and more supersized, it is important to deepen architects' understanding of the organizational logic and programming factors that inform the overall design decisions for these facilities. With a particular focus on three case studies--Coupang, E-Mart, and Amazon Fulfillment Centers, this research examines the current trends in automation and expansion of logistics centers, and offers an analysis and forecast for future facilities in South Korea.

Customer Attitude to Artificial Intelligence Features: Exploratory Study on Customer Reviews of AI Speakers (인공지능 속성에 대한 고객 태도 변화: AI 스피커 고객 리뷰 분석을 통한 탐색적 연구)

  • Lee, Hong Joo
    • Knowledge Management Research
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    • v.20 no.2
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    • pp.25-42
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    • 2019
  • AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

Performance Evaluation of IoT Cloud Platforms for Smart Buildings (스마트 빌딩을 위한 IoT 클라우드 플랫폼의 성능 평가)

  • Park, Jung Kyu;Park, Eun Young
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.5
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    • pp.664-671
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    • 2020
  • A Smart Building, one that uses automated processes to control its operations, refers in this study to one that uses both Internet of Things (IoT) devices and cloud services software. Cloud service providers (e.g. Amazon, Google, and Microsoft) have recently providedIoT cloud platform application services on IoT devices. According to Postscapes, there are now 152 IoT cloud platforms. Choosing one for a smart building is challenging. We selected Microsoft Azure IoT Hub and Amazon's AWS (Amazon Web Services) IoT. The two platforms were evaluated and selected from a smart building perspective. Each prototype was evaluated on two different IoTplatforms, assuming a typical smart building scenario. The selection was based on information and experience gained from developing the prototype system using the IoT cloud platform. The assessment made in this evaluation may be used to select an IoTcloud platform for smart buildings in the future.