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Big data storage technologies: a survey Review
Aisha SIDDIQA, Ahmad KARIM, Abdullah GANI
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8, Pages 1040-1070 doi: 10.1631/FITEE.1500441
Keywords： Big data Big data storage NoSQL databases Distributed databases CAP theorem Scalability Consistency- partition resilience Availability-partition resilience
Chao Shang、 Fengqi You
Engineering 2019, Volume 5, Issue 6, Pages 1010-1016 doi: 10.1016/j.eng.2019.01.019
Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.
Keywords： Big data Machine learning Smart manufacturing Process systems engineering
Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems Article
Junliang Wang, Peng Zheng, Youlong Lv, Jingsong Bao, Jie Zhang
Engineering 2019, Volume 5, Issue 4, Pages 662-670 doi: 10.1016/j.eng.2018.12.013
Industrial big data integration and sharing (IBDIS) is of great significance in managing and providing data for big data analysis in manufacturing systems. A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks. First, a task flow graph (TFG) is designed to model the data analysis process. The TFG is composed of several tasks, which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy. Second, the function of Fog-IBDIS to enable data integration and sharing is presented in five modules: TFG management, compilation and running control, the data integration model, the basic algorithm library, and the management component. Finally, a case study is presented to illustrate the implementation of Fog-IBDIS, which ensures raw data security by deploying the analysis tasks executed by the data generators, and eases the network traffic load by greatly reducing the volume of transmitted data.
Keywords： Fog computing Industrial big data Integration Manufacturing system
Design and Implementation of Intelligent Risk Control Platform Based on Big Data
Zhang Ming, Liu Pei
Strategic Study of CAE 2020, Volume 22, Issue 6, Pages 111-120 doi: 10.15302/J-SSCAE-2020.06.015
Since financial security is an important part of national security, controlling financial risks is the fundamental task for financial management. To help banks accelerate the establishment of risk control platforms in the era of digital economy, this study proposes an overall framework of an intelligent risk control platform with “five layers and two domains” based on the key technologies of big data. Specifically, the framework vertically consists of a risk data layer, a feature computing layer, a risk model layer, a decision engine layer, and a business access layer and all these layers are loosely coupled, stateless, and extensible. Horizontally, the framwork can be divided into a production deployment domain and a business operation domain, which considers both the stability of system operation and flexibility of business application. This design is helpful for commercial banks to realize the unified governance and management of risk data. While ensuring the efficient and stable operation of the risk control platform, it can also provide sufficient support for risk control experts in risk control operation, data analysis, model design, and rule adjustment. Finally, using the intelligent risk control platform deployed by a financial institution as an example, this study expounds the application situation and practical effect of the platform and provides some suggestions.
Keywords： risk control，big data，machine learning，real-time computation，financial industry
Perspectives on cross-domain visual analysis of cyber-physical-social big data Perspective
Wei Chen, Tianye Zhang, Haiyang Zhu, Xumeng Wang, Yunhai Wang,cloudseawang@gmail.com
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2100553
Keywords： 可视分析；三元空间；大数据；跨域
Development Status and Prospects of Ecological Environment Big Data in China
Wang Yuntao ,Wang Guoqiang ,Wang Qiao , Zhang Qingzhu
Strategic Study of CAE 2022, Volume 24, Issue 5, Pages 56-62 doi: 10.15302/J-SSCAE-2022.05.021
Ecological environment big data can further support the construction of ecological civilization and the construction of a beautiful China by modernizing the ecological environment governance system and capabilities. However, China’s eco-environmental big data strategy is difficult to implement because of mentality and technical bottlenecks. Facing the urgent demand of ecological environment protection for ecological environment big data during the 14th Five-Year period, this article summarizes the development status of ecological environment big data in China and analyzes the existing problems from three aspects: mechanism construction, technology research and development (R&D), and business support. Six key directions are presented: intelligent perception and problem identification of ecological environment; mining of evolution law and driving mechanism; traceability analysis of environmental pollution and ecosystem damages; scenario simulation and prediction evaluation; risk early warning and emergency decision-making; supervision and performance evaluation. Countermeasures are proposed focusing on management mechanism, data resource awareness, technology R&D and demonstration, capital investment, and talent training, thus to support the high-quality development of big data regarding ecological environment during the 14th Five-Year period.
Keywords： ecological environment big data coordination mechanism resource awareness
A Product Process Adaptive Design Method Based on Manufacturing-Related Big Data
Wei Wei, Chen Zheng, Yuan Jun
Strategic Study of CAE 2020, Volume 22, Issue 4, Pages 42-49 doi: 10.15302/J-SSCAE-2020.04.017
As digital and smart production methods being applied widely in manufacturing, enterprises should pay more attention to the values of manufacturing-related big data, which is important to the innovation of product process design. This study aims to propose a product process adaptive design method based on manufacturing-related big data. This method is proposed based on the requirement of enterprises for data–business deep integration and it is used for solving the insufficient utilization of manufacturing-related data among enterprises. To this end, a product process adaptive design model “data + knowledge + decision” is proposed and the data mining and utilization processes are summarized for the model, namely, multi-source heterogeneous data fusion; data cleaning and preprocessing; data conversion and dimensionality reduction; data mining; data visualization; and design decision. Subsequently, the automobile welding process is used as an example. A prediction model of the relationship between welding parameters and welding defects is established, aiming to improve welding quality and realize welding process adaptive design. This research reveals that manufacturing related big data contains rich knowledge and patterns and thus can guide product design decisions and support the product process adaptive design under different manufacturing environments. In the future, to enhance the integration of manufacturing-related big data with product process design, the integration of big data with 5G technology should be promoted and investment should be increased inthe development of big data and algorithm design platforms.
Keywords： product process adaptive design manufacturing-related big data data mining knowledge discovery
Teng Zhou, Zhen Song, Kai Sundmacher
Engineering 2019, Volume 5, Issue 6, Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011
Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided.
Keywords： Big data Data-driven Machine learning Materials screening Materials design
Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM Article
Bowen Du, Yanliang Du, Fei Xu, Peng He
Engineering 2018, Volume 4, Issue 1, Pages 123-130 doi: 10.1016/j.eng.2017.07.002
The New Austrian Tunneling Method (NATM) has been widely used in the construction of mountain tunnels, urban metro lines, underground storage tanks, underground power houses, mining roadways, and so on. The variation patterns of advance geological prediction data, stress–strain data of supporting structures, and deformation data of the surrounding rock are vitally important in assessing the rationality and reliability of construction schemes, and provide essential information to ensure the safety and scheduling of tunnel construction. However, as the quantity of these data increases significantly, the uncertainty and discreteness of the mass data make it extremely difficult to produce a reasonable construction scheme; they also reduce the forecast accuracy of accidents and dangerous situations, creating huge challenges in tunnel construction safety. In order to solve this problem, a novel data service system is proposed that uses data-association technology and the NATM, with the support of a big data environment. This system can integrate data resources from distributed monitoring sensors during the construction process, and then identify associations and build relations among data resources under the same construction conditions. These data associations and relations are then stored in a data pool. With the development and supplementation of the data pool, similar relations can then be used under similar conditions, in order to provide data references for construction schematic designs and resource allocation. The proposed data service system also provides valuable guidance for the construction of similar projects
Keywords： New Austrian Tunneling Method Big data environments Data as a service Tunnel construction
Big Data for Precision Medicine
Daniel Richard Leff, Guang-Zhong Yang
Engineering 2015, Volume 1, Issue 3, Pages 277-279 doi: 10.15302/J-ENG-2015075
This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. We examine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.
Keywords： big data biosensors body-sensing networks implantable sensors clinical decision support systems pharmacogenetics mHealth
Technology and Equipment of Big Data on Crop Phenomics
Wen Weiliang , Guo Xinyu, Zhang Ying , Gu Shenghao, Zhao Chunjiang
Strategic Study of CAE 2023, Volume 25, Issue 4, Pages 227-238 doi: 10.15302/J-SSCAE-2023.04.015
Automatic equipment and information technologies make it possible to acquire multi-scale and multi-source heterogeneous data of crops under different growth conditions, forming big data on crop phenomics. This will greatly promote the research progress of crop functional genomics, digital breeding, and smart cultivation. In this paper, the demand for and industrial development of technology and equipment of big data on crop phenomics are analyzed. Then, the current situation of research and development in this area is summarized from five aspects: data acquisition hardware, data transmission, data analysis, knowledge formation, and applications. The problems and developmental trends of relevant technologies, equipment, and industrial application in China are analyzed from the perspectives of high-throughput acquisition and intelligent analysis of big data on crop phenomics. At last, the following suggestions are proposed: achieving breakthroughs regarding key crop phenotyping sensor technologies from the underlying chip level, forming an autonomous phenotyping extraction technology system on the basis of controllable open source, strengthening the standards system construction for big data on crop phenomics, creating a new model of genotype‒phenotype‒environment big data-driven digital breeding and smart cultivation, and building a talent pool and collaborative network for crop phenomics.
Keywords： crop phenomics phenotyping big data technology and equipment for phenotyping multi-omics
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 221-231 doi: 10.15302/J-SSCAE-2023.07.005
Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical values. In this study, following an analysis of the development status and challenges facing China’s intelligent taxation field, we proposed a big data knowledge engineering approach that emphasizes data knowledgeization, knowledge systematization, and knowledge reasonability, and developed a five-layer technical architecture that consists of knowledge sources, knowledge extraction, knowledge mapping, knowledge reasoning, and application layers. After elaborating the representative application scenarios including knowledge-driven tax preference calculation, interpretable tax risk identification, intelligent decision support for tax policies, and smart tax questioning,we investigated the limitations of the proposed approach and further discussed the directions for future research. Furthermore, we proposed the following development suggestions in terms of data, technology, and ecology: (1) standardizing tax-related information and improving the national data sharing, opening, and guarantee system; (2) integrating the achievements of various information disciplines and improving the application system of big data knowledge engineering for smart taxation; and (3) promoting talent training and the development of technical standards for big data knowledge engineering.
Keywords： smart taxation knowledge engineering big data knowledge graph knowledge reasoning
Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2100463
Keywords： 多重知识表达；人工智能；大数据
Big Data Research in Italy: A Perspective Perspective
Sonia Bergamaschi,Emanuele Carlini,Michelangelo Ceci,Barbara Furletti,Fosca Giannotti,Donato Malerba,Mario Mezzanzanica,Anna Monreale,Gabriella Pasi,Dino Pedreschi,Raffele Perego,Salvatore Ruggieri
Engineering 2016, Volume 2, Issue 2, Pages 163-170 doi: 10.1016/J.ENG.2016.02.011
The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.
Keywords： Big data Smart cities Energy Job offers Privacy
Software Architecture of Fogcloud Computing for Big Data in Ubiquitous Cyberspace
Jia Yan, Fang Binxing, Wang Xiang, Wang Yongheng, An Jingbin,Li Aiping, Zhou Bin
Strategic Study of CAE 2019, Volume 21, Issue 6, Pages 114-119 doi: 10.15302/J-SSCAE-2019.10.001
The cyberspace has expanded from traditional internet to ubiquitous cyberspace which interconnects human, machines,things, services, and applications. The computing paradigm is also shifting from centralized computing in the cloud to combined computing in the front end, middle layer, and cloud. Therefore, traditional computing paradigms such as cloud computing and edge computing can no longer satisfy the evolving computing needs of big data in ubiquitous cyberspace. This paper presents a computing architecture named Fogcloud Computing for big data in ubiquitous cyberspace. Collaborative computing by multiple knowledge actors in the fog, middle layer, and cloud is realized based on the collaborative computing language and models, thereby providing a solution for big data computing in ubiquitous cyberspace.
Keywords： fogcloud computing ubiquitous cyberspace big data Internet of Things cloud computing
Title Author Date Type Operation
Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era
Chao Shang、 Fengqi You
Journal Article
Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems
Junliang Wang, Peng Zheng, Youlong Lv, Jingsong Bao, Jie Zhang
Journal Article
Design and Implementation of Intelligent Risk Control Platform Based on Big Data
Zhang Ming, Liu Pei
Journal Article
Perspectives on cross-domain visual analysis of cyber-physical-social big data
Wei Chen, Tianye Zhang, Haiyang Zhu, Xumeng Wang, Yunhai Wang,cloudseawang@gmail.com
Journal Article
Development Status and Prospects of Ecological Environment Big Data in China
Wang Yuntao ,Wang Guoqiang ,Wang Qiao , Zhang Qingzhu
Journal Article
A Product Process Adaptive Design Method Based on Manufacturing-Related Big Data
Wei Wei, Chen Zheng, Yuan Jun
Journal Article
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design
Teng Zhou, Zhen Song, Kai Sundmacher
Journal Article
Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM
Bowen Du, Yanliang Du, Fei Xu, Peng He
Journal Article
Technology and Equipment of Big Data on Crop Phenomics
Wen Weiliang , Guo Xinyu, Zhang Ying , Gu Shenghao, Zhao Chunjiang
Journal Article
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Journal Article
Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn
Journal Article
Big Data Research in Italy: A Perspective
Sonia Bergamaschi,Emanuele Carlini,Michelangelo Ceci,Barbara Furletti,Fosca Giannotti,Donato Malerba,Mario Mezzanzanica,Anna Monreale,Gabriella Pasi,Dino Pedreschi,Raffele Perego,Salvatore Ruggieri
Journal Article
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