• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1259-1264
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• 2019

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2682-2700
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• 2023

To cope with the risks of climate change and promote the realization of carbon peaking and carbon neutrality, this paper first comprehensively considers the policy background, technical trends and carbon reduction paths of energy conservation and emission reduction in data center server industry. Second, we propose a computing power carbon efficiency of data center server, and constructs the carbon emission per performance of server (CEPS) model. According to the model, this paper selects the mainstream data center servers for testing. The result shows that with the improvement of server performance, the total carbon emissions are rising. However, the speed of performance improvement is faster than that of carbon emission, hence the relative carbon emission per unit computing power shows a continuous decreasing trend. Moreover, there are some differences between different products, and it is calculated that the carbon emission per unit performance is 20-60KG when the service life of the server is five years.

• 디지털융복합연구
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• 제12권6호
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• pp.341-347
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• 2014

빅데이터를 처리할 수 있는 방안 중에 클라우드 환경에서의 빅데이터 처리가 주목할 만한 대안으로 자리 잡아가고 있다. 의료 보건산업에서도 패러다임의 변화와 증가하는 의료비에 대한 절감 압박, 서비스의 수준에 대한 소비자의 관심 증대 등 당면한 문제 해결과 산업 경쟁력 강화 방안의 일환으로 빅데이터 활용 방안에 대한 논의가 활발히 이루어지고 있다. 이에 본 논문에서는 클라우드와 빅데이터와의 관계를 알아보고, 클라우드 기반의 의료 분야에서의 빅데이터 활용사례를 조사 분석하여 효율적인 활용방안과 이를 위한 전망을 제시하고자 한다. 클라우드 기반의 의료 빅데이터의 원활한 운영을 위해서는 인프라 확충과 분석 응용 소프트웨어의 개발, 전문 인력 양성 등의 문제를 해결해야 할 것으로 보인다. 또한, 클라우드 활용에 있어 미비한 법 제도의 정비, 개인정보에 대한 보안기술 및 인식 개선, 데이터의 집중에 따른 권력화 등이 해결해야 할 과제이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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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.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.43-55
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• 2021

The amount of Big Data generated from multiple sources is continuously increasing. Traditional storage methods lack the capacity for such massive amounts of data. Consequently, most organizations have shifted to the use of cloud storage as an alternative option to store Big Data. Despite the significant developments in cloud storage, it still faces many challenges, such as privacy and security concerns. This paper discusses Big Data, its challenges, and different classifications of security and privacy challenges. Furthermore, it proposes a new classification of Big Data security and privacy challenges and offers some perspectives to provide solutions to these challenges.

• 한국콘텐츠학회논문지
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• 제15권5호
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• pp.570-581
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• 2015

최근 많은 기관들로부터 클라우드 서비스, 빅 데이터가 향후 대세적인 IT 트렌드 및 확고한 기술로서 예견되고 있다. 또한 현재 IT를 선도하는 많은 벤더를 중심으로 클라우드, 빅데이터에 대한 실질적인 솔루션과 서비스를 제공하고 있다. 이러한 기술들은 기업의 비용절감 측면에서, 클라우드는 인터넷 기반의 다양한 기술들을 기반으로 비즈니스 모델에 대한 자원의 사용을 자유스럽게 선택할 수 있는 장점을 가지고 있어 능동적인 자원 확장을 위한 프로비져닝 기술과 가상화 기술들이 주요한 기술로 주목 받고 있다. 또한 빅데이터는 그동안 분석하지 못했던 새로운 비정형 데이터들에 대한 분석 환경을 제공함으로서 데이터 예측모델의 차원을 한층 높이고 있다. 하지만 클라우드 서비스, 빅데이터의 공통점은 대용량 데이터를 기반으로 서비스 또는 분석을 요하고 있어, 초기 발전 모델부터 대용량 데이터의 효율적인 운영 및 설계가 중요하게 대두 되고 있다. 따라서 본 논문에 클라우드, 빅데이터 서비스를 위한 대용량 데이터 기술 요건들을 토대로 데이터 처리 아키텍처를 정립하고자 한다. 특히, 클라우드 컴퓨팅을 위해 분산 파일 시스템이 갖추어야 할 사항들과 클라우드 컴퓨팅에서 활용 가능한 오픈소스 기반의 하둡 분산 파일 시스템, 메모리 데이터베이스 기술요건을 소개하고, 빅데이터, 클라우드의 대용량 데이터를 비용절감 측면에서 효율적인 압축기술 요건들을 제시한다.

• 한국측량학회지
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• 제34권2호
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• pp.207-215
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• 2016

The demand for spatial data service technologies is increasing lately with the development of realistic 3D spatial information services and ICT (Information and Communication Technology). Research is being conducted on the real-time provision of spatial data services through a variety of mobile and Web-based contents. Big data or cloud computing can be presented as alternatives to the construction of spatial data for the effective use of large volumes of data. In this paper, the process of building HLM (Hyper Live Map) using multi-source data to acquire stereo CCTV and other various data is presented and a big data service architecture design is proposed for the use of flexible and scalable cloud computing to handle big data created by users through such media as social network services and black boxes. The provision of spatial data services in real time using big data and cloud computing will enable us to implement navigation systems, vehicle augmented reality, real-time 3D spatial information, and single picture based positioning above the single GPS level using low-cost image-based position recognition technology in the future. Furthermore, Big Data and Cloud Computing are also used for data collection and provision in U-City and Smart-City environment as well, and the big data service architecture will provide users with information in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.204-226
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• 2018

Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.