MISS-D: A fast and scalable framework of medical image storage service based on distributed file system
MISS-D: یک چارچوب سریع و مقیاس پذیر از خدمات ذخیره سازی تصویر پزشکی بر اساس سیستم فایل توزیع شده-2020
Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging
Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
الگوریتم تکاملی چند هدفی مبتنی بر شبکه عصبی برای زمانبندی گردش کار پویا در محاسبات ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 45
زمانبندی گردشکار یک موضوع پژوهشی است که به طور گسترده در محاسبات ابری مورد مطالعه قرار گرفته است و از منابع ابری برای کارهای گردش کار استفاده می¬شود و برای این منظور اهداف مشخص شده در QoS را لحاظ می¬کند. در این مقاله، مسئله زمانبندی گردش کار پویا را به عنوان یک مسئله بهینه سازی چند هدفه پویا (DMOP) مدل می¬کنیم که در آن منبع پویایی سازی بر اساس خرابی منابع و تعداد اهداف است که ممکن است با گذر زمان تغییر کنند. خطاهای نرم افزاری و یا نقص سخت افزاری ممکن است باعث ایجاد پویایی نوع اول شوند. از سوی دیگر مواجهه با سناریوهای زندگی واقعی در محاسبات ابری ممکن است تعداد اهداف را در طی اجرای گردش کار تغییر دهد. در این مطالعه یک الگوریتم تکاملی چند هدفه پویا مبتنی بر پیش بینی را به نام الگوریتم NN-DNSGA-II ارائه می¬دهیم و برای این منظور شبکه عصبی مصنوعی را با الگوریتم NGSA-II ترکیب می¬کنیم. علاوه بر این پنج الگوریتم پویای مبتنی بر غیرپیش بینی از ادبیات موضوعی برای مسئله زمانبندی گردش کار پویا ارائه می¬شوند. راه¬حل¬های زمانبندی با در نظر گرفتن شش هدف یافت می¬شوند: حداقل سازی هزینه ساخت، انرژی و درجه عدم تعادل و حداکثر سازی قابلیت اطمینان و کاربرد. مطالعات تجربی مبتنی بر کاربردهای دنیای واقعی از سیستم مدیریت گردش کار Pegasus نشان می¬دهد که الگوریتم NN-DNSGA-II ما به طور قابل توجهی از الگوریتم¬های جایگزین خود در بیشتر موارد بهتر کار می¬کند با توجه به معیارهایی که برای DMOP با مورد واقعی پارتو بهینه در نظر گرفته می¬شود از جمله تعداد راه¬حل¬های غیرغالب، فاصله¬گذاری Schott و شاخص Hypervolume.
|مقاله ترجمه شده|
Manufacturing big data ecosystem: A systematic literature review
ساخت اکوسیستم داده های بزرگ: مروری بر ادبیات سیستماتیک-2020
Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL
Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives
خوشه بندی حفظ حریم خصوصی برای داده های بزرگ در سیستم های سایبر-فیزیکی-اجتماعی: بررسی و چشم انداز-2020
Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized rec- ommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing meth- ods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and stor- age. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multi- ple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.
Keywords: CPSS | Big data | Cloud computing | Privacy preserving | Clustering
Towards DNA based data security in the cloud computing environment
به سمت امنیت داده های مبتنی بر DNA در محیط محاسبات ابری-2020
Nowadays, data size is increasing day by day from gigabytes to terabytes or even petabytes, mainly because of the evolution of a large amount of real-time data. Most of the big data is transmitted through the internet and they are stored on the cloud computing environment. As cloud computing provides internet-based services, there are many attackers and malicious users. They always try to access user’s confidential big data without having the access right. Sometimes, they replace the original data by any fake data. Therefore, big data security has become a significant concern recently. Deoxyribonucleic Acid (DNA) computing is an advanced emerged field for improving data security, which is based on the biological concept of DNA. A novel DNA based data encryption scheme has been proposed in this paper for the cloud computing environment. Here, a 1024-bit secret key is generated based on DNA computing, user’s attributes and Media Access Control (MAC) address of the user, and decimal encoding rule, American Standard Code for Information Interchange (ASCII) value, DNA bases and complementary rule are used to generate the secret key that enables the system to protect against many security attacks. Experimental results, as well as theoretical analyses, show the efficiency and effectivity of the proposed scheme over some well-known existing schemes.
Keywords: Cloud computing | DNA computing | Big data security | MAC address | Complementary rule | CloudSim
Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy
هوش لبه مبتنی بر اجرای اقتصادی برای نیروگاه مجازی در اینترنت 5G انرژی-2020
Nowadays, with a large of complicated geography of Distributed Energy Sources (DES), how to integrate distributed renewable energy source and reduce the operational costs by Virtual Power Plant (VPP) becomes a mainstream problem in Internet of energy. The traditional method of energy integration and operational cost optimization utilizes the cloud computing technology to centralized control the computational task, which increases the burden of computing. According with the development of information communication technology, such as Internet of Things and 5G, edge computing technology is an effective way to offload computational task to the edge side of 5G networks. Moreover, with the increase of collected data, it becomes a key point to effectively improve the computing power of edge nodes in edge computing. Currently, machine learning is an effective way to process the big data. Based this situation, it leads the combination of machine learning and edge computing. In this paper, the Edge Intelligence (EI) structure is proposed to solve the Economic Dispatch Problem (EDP) in VPP of Internet of Energy. Compared with the traditional edge computing, the proposed EI structure inherits its original features which reduce the burden of cloud computing, and also the proposed EI structure improves the computational power of edge computing. Through the splitting model and deploying the particle model in the terminal, it is facility to real-time control and take the less costs of VPP. Due to the transmission between the splitting models with counterpart, it transmits the part information and gradient information, which effectively reduces the consumption of communication. The proposed method has verified the effectiveness and feasibility through the numerical experiments of real application data sets.
Keywords: Virtual Power Plant | Machine leaning | Edge intelligence | Economic Dispatch
Industry 4:0 based process data analytics platform: A waste-to-energy plant case study
پلت فرم تجزیه و تحلیل داده های مبتنی بر فرآیند صنعت 4:0: مطالعه موردی از گیاهان زباله به انرژی-2020
Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. The industrial applicability of the platform was demonstrated by the development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied datadriven soft sensors to predict syngas heating value and hot flue gas temperature. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.
Keywords: Data analytics platform | Industrial internet of things platform | Machine learning | Waste-to-energy | Soft sensor
Model-based vehicular prognostics framework using Big Data architecture
چارچوب پیش آگهی های وسایل نقلیه مبتنی بر مدل با استفاده از معماری داده های بزرگ-2020
Nowadays, the continuous technological advances allow designing novel Integrated Vehicle Health Man-agement (IVHM) systems to deal with strict safety regulations in the automotive field with the aim atimproving efficiency and reliability of automotive components. However, challenging issue, which arisesin this domain, is handling a huge amount of data that are useful for prognostic. To this aim, in thispaper we propose a cloud-based infrastructure, namely Automotive predicTOr Maintenance In Cloud(ATOMIC), for prognostic analysis that leverages Big Data technologies and mathematical models of bothnominal and faulty behaviour of the automotive components to estimate on-line the End-Of-Life (EOL)and Remaining Useful Life (EUL) indicators for the automotive systems under investigation. A case studybased on the Delphi DFG1596 fuel pump has been presented to evaluate the proposed prognostic method.Finally, we perform a benchmark analysis of the deployment configurations of ATOMIC architecture interms of scalability and cost.
Keywords:Model-based prognostic analysis | Big Data analysis | Cloud computing servicesa
Heterogeneous edge computing open platforms and tools for internet of things
پلت فرم ها و ابزارهای باز محاسبات لبه ناهمگن برای اینترنت اشیا-2020
With the continuous development of Internet of Things (IoT) and the overwhelming explosion of Big Data, edge computing serves as an efficient computing mode for time stringent data processing, which can bypass the constraints of network bandwidth and delay, and has been one of the foundation of interconnected applications. Although edge computing has gradually become one of bridges between cloud computing centers and mobile terminals, the literature still lacks a thorough review on the recent advances in edge computing platforms. In this paper, we firstly introduce the definition of edge computing and advantages of edge computing platform. And then, we summarize the key technologies of constructing an edge computing platform, and propose a general framework for edge computing platform. The role of distributed storage management systems in building edge computing platform is elaborated in detail. Furthermore, we give some applications to illustrate how to use third-party edge computing platforms to build specific applications. Finally, we briefly outline current open issues of edge computing platform based on our literature survey.
Keywords: Edge computing | Cloud computing | Platform | Internet of Things(ioT) | Architecture | Application