Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction

Zhang, Xianyu; Ming, Xinguo; Yin, Dao

doi:10.1007/s00170-019-04374-x

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…where a (l) represents the automatic encoder, and z (l) represents the coding parameters, and conversely, the decoding of each layer of automatic encoder is performed from back to front. 2) .…”

Section: Big Data Of Urban Leisure and Fitnessmentioning

confidence: 99%

“…If the deep learning model is a 4-layer network training model, taking (x (1), y (1)) as the network training set, the forward propagation algorithm is briefly described as a (1) � x, z (2) � θ (1) a (1) , a (2) � g z (2) 􏼐 􏼑 a d da (2) 0 􏼐 􏼑,…”

Section: Big Data Of Urban Leisure and Fitnessmentioning

confidence: 99%

“…Urban leisure physical culture healthy is a basic project to realize the fundamental task of educating people and improving their comprehensive quality. At present, there is an urgent need for a mega data service platform to adapt to the healthy development of urban leisure sports and to address a knowledge gap in the building of urban leisure physical culture [2,3]. e basis of the big data sector is urban leisure, sports, and health data.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Construction of Urban Leisure Physical Culture Healthy Big Data Service Platform Based on In-Depth Learning

Ji-fang

Jianguo

2022

Mobile Information Systems

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The national fitness service system must focus on meeting the sports needs of residents, innovate the construction mode of urban leisure sports big data service platform, and promote the construction of urban leisure sports health big data service platform. The healthy development of urban leisure sports should also adhere to the principles of demand orientation and problem awareness. Big data is an industry with urban leisure and sports health as its core data. It is a production process that continues to cycle in the life cycle of big data. Therefore, this paper uses in-depth learning to study the construction of big data service platform for urban leisure sports health. The in-depth learning improvement project is an expert team organized by the urban leisure sports health basic textbook development center of the Ministry of education. The data revolution driven by deep learning examined the value of data, conquered the data ocean, and created a new era of personalized medicine in the fields of urban leisure, sports, and health. On the basis of in-depth learning, this paper constructs a big data service platform for urban leisure sports health. On the one hand, the establishment of this platform will help to speed up the construction process of sports service informatization and the reform of various fields of sports, on the other hand, it will help to promote the improvement of the physical quality of the whole people.

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Section: Big Data Of Urban Leisure and Fitnessmentioning

confidence: 99%

Section: Big Data Of Urban Leisure and Fitnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on the Construction of Urban Leisure Physical Culture Healthy Big Data Service Platform Based on In-Depth Learning

Ji-fang

Jianguo

2022

Mobile Information Systems

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“…Reference architectures [25] aim at providing abstract software architectures that collect architectural patterns and software elements for supporting the development of systems in specific domains. With regard to big data systems, several authors have proposed reference architectures for big data systems [26][27][28], and reference architectures for big data systems in specific domains, such as security [29], industry [30], e-learning [31], cloud-based video analytics [32], and smart cities [33], to name a few.…”

Section: Nist Big Data Reference Architecturementioning

confidence: 99%

“…Other techniques frequently used are text summarization and semantic enrichment, mainly based on entity recognition and linking. Zhang et al [30] carried out an interdisciplinary review of social media use in disasters. They point out the need to analyze temporal and spatial patterns to understand the information diffusion process using social network analysis techniques, providing valuable insights for understanding the rumor propagation process.…”

Section: Digital Humanitarianism In Disastersmentioning

confidence: 99%

A Big Data Reference Architecture for Emergency Management

2020

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Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems.

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Application of security reference architecture to Big Data ecosystems in an industrial scenario

et al. 2020

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Big Data environments are typically very complex ecosystems; this means that implementing them is complicated. One possible technique with which to address this complexity is the use of abstraction. Reference architecture (RA) can be useful for an improved understanding of the main components of Big Data. Herein, we propose a security RA that includes the management of security concerns and provides the main elements of a Big Data ecosystem. Application of this architecture to real-world scenarios facilitates its refinement and improves its usefulness. In this article, we present a case study of a real-world Big Data ecosystem implemented in a banking environment. This ecosystem was developed by everis, an NTT company with which we collaborated for this study. To conduct this validation case study, a map was established between the elements of the Big Data ecosystem implemented and our proposal. Consequently, a series of valuable lessons that can improve both our architecture and the security of the Big Data environment were obtained. These include recommendations for a set of best practices such as the use of security patterns.

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Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction

Cited by 16 publications

References 39 publications

Research on the Construction of Urban Leisure Physical Culture Healthy Big Data Service Platform Based on In-Depth Learning

Research on the Construction of Urban Leisure Physical Culture Healthy Big Data Service Platform Based on In-Depth Learning

A Big Data Reference Architecture for Emergency Management

Application of security reference architecture to Big Data ecosystems in an industrial scenario

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