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big data management in the mining industry

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International Journal of Minerals,Metallurgy and Materials 2020年第2期27卷 131-139页

作者： Chong-chong Qi School of Resources and Safety Engineering Central South University School of Civil Environmental and Mining Engineering University of Western Australia

The mining industry faces a number of challenges that promote the adoption of new *** data,which is driven by the accelerating progress of information and communication technology,is one of the promising technologies that can reshape the entire mining *** numerous attempts to apply big data in the mining industry,fundamental problems of big data,especially big data management(BDM),in the mining industry *** paper aims to fill the gap by presenting the basics of *** work provides a brief introduction to big data and BDM,and it discusses the challenges encountered by the mining industry to indicate the necessity of implementing big *** also summarizes data sources in the mining industry and presents the potential benefits of big data to the mining *** work also envisions a future in which a global database project is established and big data is used together with other technologies(i.e.,automation),supported by government policies and following international *** paper also outlines the precautions for the utilization of BDM in the mining industry.

关键词： big data big data management mining industry

big data in smart cities

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Science China(Information Sciences) 2015年第10期58卷 179-190页

作者： LI DeRen CAO JianJun YAO Yuan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Collaborative Innovation Center of Geospatial Technology Geomatics Center of Gansu Province National Engineering Research Center for Multimedia Software Computer School Wuhan University

In this paper, we discuss the concept of the smart city, summarize its development, analyze the motivation and goals of building smart cities in China, and illustrate the supporting technologies of the smart city. Next, we propose a smart city infrastructure that is based on the concept of the digital city and integrates the Internet of Things and cloud computing technologies, thereby achieving automatic control and intelligence services for people and logistics in physical cities. We analyze the big data of smart cities according to ubiquitous sensor networks. Facing the problems and challenges caused by big data of smart cities, we propose a strategy to handle big data that focuses on cloud computing and data mining. In addition, we present a basic framework for cloud computing platforms and propose the establishment of an operating center for smart cities. In closing,we discuss the bright prospects of smart cities.

关键词： smart city big data digital city Internet of Things cloud computing data mining intelligence service

big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

big data and the future of urban ecology:From the concept to results

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Science China Earth Sciences 2020年第10期63卷 1443-1456页

作者： Jun YANG Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Beijing 100084China

Urban ecology is experiencing the third paradigm *** understand the interactions between the social system and the natural system in the city across time and space,and to provide theories and solutions to sustainable urban development are essential tasks for urban ecology in the next *** data can play a crucial role in future urban ecology studies due to the interdisciplinary nature of urban ecology,the fact that cities are factories of big data,and the new insights gained by using big data in ***,to translate big data from a concept to research results that can guide planning,policymaking,and management of cities,we need to overcome multiple challenges existing in the theoretical framework,data acquisition,and analytic *** ecologists should enhance the collaboration with the data scientists to increase the application of big data in studies of urban biodiversity,urban ecosystem services and human wellbeing,and processes of urban ecosystems.

关键词： big data Social media Research paradigm Sustainable development Urban ecosystem

big data:UNLEASHING INFORMATION

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Journal of Systems Science and Systems Engineering 2013年第2期22卷 127-151页

作者： James M.TIEN College of Engineering University of Miami

At present, it is projected that about 4 zettabytes （or 10^＊＊21 bytes） of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors （i.e., services, manufacturing, construction, agriculture and mining） of the economy. big data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien （2003） forewarned about the data rich, information poor （DRIP） problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the big data approach which has unleashed information in a manner that can support informed - yet, not necessarily defensible or valid - decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further big data as it is being employed to help resolve the 14 grand challenges （identified by the National Academy of Engineering in 2008）, underpin the 10 breakthrough technologies （compiled by the Massachusetts Institute of Technology in 2013） and support the Third Industrial Revolution of mass customization.

关键词： big data data acquisition data access data analytics data application decision informatics products processes adaptive services digital manufacturing mass customization third industrial revolution

big data-driven automatic generation of ship route planning in complex maritime environments

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Acta Oceanologica Sinica 2020年第8期39卷 113-120页

作者： Peng Han Xiaoxia Yang Administrative Center for China’s Agenda 21 Beijing 100038China College of Earth Science Chengdu University of TechnologyChengdu 610059China

With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.

关键词： ship route planning AIS big data trajectory data mining electronic chart

big data Analytics Using Graph Signal Processing

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Computers, Materials & Continua 2023年第1期74卷 489-502页

作者： Farhan Amin Omar M.Barukab Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Computing and Information Technology-Rabigh King Abdulaziz UniversityJeddahSaudi Arabia

The networks are fundamental to our modern world and they appear throughout science and *** to a massive amount of data presents a unique opportunity to the researcher’s *** networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep ***,this paper initiates a discussion on graph signal processing for large-scale data *** first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,*** filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of *** then consider a graph filter as a model that helps to extend the application of GSP methods to large *** show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the *** several rounds of simulation *** see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based *** using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

关键词： big data data science big data processing graph signal processing social networks

big data Access Control Mechanism Based on Two-Layer Permission Decision Structure

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Computers, Materials & Continua 2024年第4期79卷 1705-1726页

作者： Aodi Liu Na Wang Xuehui Du Dibin Shan Xiangyu Wu Wenjuan Wang He’nan Province Key Laboratory of Information Security Information Engineering UniversityZhengzhou450000China

big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business *** BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

关键词： big data access control data security BiLSTM

big data Security and Privacy： A Review

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China Communications 2014年第A02期11卷 135-145页

作者： MATTURDI Bardi ZHOU Xianwei LI Shuai LIN Fuhong School of Mathematics and Information Hotan Teachers College Hetian 848000 Xinjiang P. R. China School of Computer and Communication Engineering University of Science and Technology B eijing Beijing 100083 P. R. China

While big data gradually become a hot topic of research and business and has been everywhere used in many industries, big data security and privacy has been increasingly concerned. However, there is an obvious contradiction between big data security and privacy and the widespread use of big data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in big data. Then, we present some possible methods and techniques to ensure big data security and privacy.

关键词： big data value of big data security and privacy