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Journal of Ocean University of China 2021年第1期20卷 228-234页

作者： GUAN Huanhuan ZHAO Xueli Management College Ocean University of ChinaQingdao 266100China College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

In recent years,China has paid more and more attention to the development of marine economy and the management and protection of fishery *** management departments at all levels regulate and manage the fishing behavior of fishing vessels through the data of fishing *** this paper,the distribution of shrimp farms in the East China Sea is predicted by studying the trajectories and behavior patterns of shrimp boats in the system of fishing *** the same time,a set of shrimp farm distribution management system based on Back Propagation algorithm is *** can monitor the trajectories of fishing boats and the distribution of shrimp groups in real time,which effectively improves the work efficiency and management mode of the management *** also plays a positive role in regulating the behavior of fishing boats at sea.

关键词： BP algorithm big data shrimp farm distribution fishery management

Self-Tuning Parameters for Decision Tree Algorithm Based on big data Analytics

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Computers, Materials & Continua 2023年第4期75卷 943-958页

作者： Manar Mohamed Hafez Essam Eldin F.Elfakharany Amr A.Abohany Mostafa Thabet College of Computing and Information Technology Arab Academy for ScienceTechnology&Maritime TransportCairoEgypt Faculty of Computers and Information Kafrelsheikh UniversityKafrelsheikhEgypt Faculty of Computers and Information Fayoum UniversityFayoumEgypt

big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.

关键词： big data classification decision tree Amazon web services

Privacy-Preserving Deep Learning on big data in Cloud

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China Communications 2023年第11期20卷 176-186页

作者： Yongkai Fan Wanyu Zhang Jianrong Bai Xia Lei Kuanching Li State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China Industrial and Commercial Bank of China Zhuhai Branch Zhuhai 519000China Dept.of Computer Science and Information Engineering Providence UniversityTaichung 43301China

In the analysis of big data,deep learn-ing is a crucial *** data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage ***-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their *** use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving *** preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)***,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by ***,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy *** with Non-Privacy Preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.

关键词： big data cloud computing deep learning homomorphic encryption privacy-preserving

Revisiting the death of geography in the era of big data:the friction of distance in cyberspace and real space

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International Journal of Digital Earth 2018年第5期11卷 451-469页

作者： Su Yeon Han Ming-Hsiang Tsou Keith C.Clarke Department of Geography San Diego State UniversitySan DiegoCAUSA Department of Geography University of CaliforniaSanta BarbaraCAUSA

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet,while others disagree with this *** have noted the distance decay effect in both cyberspace and real space,showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the ***,these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace,nor do they quantify the differences in the amount of friction of distance between cyberspace and real *** fill this gap,this study used massive amounts of social media data(Twitter)to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative *** estimate the distance decay effect in both cyberspace and real space,the distance decay function of interactions in each space was *** the distance decay in cyberspace in this study can help predict the degree of information flow across space through social *** how far ideas can be diffused through social media is useful for users of location-based services,policy advocates,public health officials,and political campaigners.

关键词： First law of geography distance decay big data Twitter social media information diffusion

Significance and methodology:Preprocessing the big data for machine learning on TBM performance

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Underground Space 2022年第4期7卷 680-701页

作者： Hao-Han Xiao Wen-Kun Yang Jing Hu Yun-Pei Zhang Liu-Jie Jing Zu-Yu Chen Department of Geotechnical Engineering China Institute of Water Resources and Hydropower ResearchBeijing 100038China Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China Institute of Underground Space School of Civil EngineeringSoutheast UniversityNanjing 211189China Department of Hydraulic Engineering Tsinghua UniversityBeijing 100084China China Railway Engineering Equipment Group Co. Ltd.ZhengzhouHenan 450016China

This paper addresses the significance of preprocessing big data collected during a tunnel boring machine(TBM)excavation before it is used for machine learning on various TBM performance *** research work is based on two water diversion tunneling projects that cover 29.52 km and 17051 boring *** has been found that the penetration rate calculated from the raw measured penetration distances exhibits more random behavior owing to their percussive and vibratory behavior of the cutterhead.A moving average method to process the negative instantaneous velocities and a noise reduction filter to deal with signals with abnormal frequencies have been *** index called the drilling efficiency index is introduced to assess the relationships between the mechanical parameters in a boring cycle,whose linear regression coefficient R^(2)is taken for a preliminary investigation of possible problems requiring *** research work defines the irrelevant data whose errors are caused by human or mechanical mistakes,and therefore should be cleaned or *** irrelevant data can be divided into five categories:(1)premature cycles,(2)sensor defects,(3)mechanical defects,(4)human interruption,and(5)missing files.A program TBM-Processing has been coded for the recognition and classification of these *** books generated by the program have been uploaded at GitHub to encourage discussions,collaboration,and upgrading of the data processing work with our peers.

关键词： TBM big data data processing Anomaly classification Machine learning

Coping with the big data:Convergence of Communications,Computing and Storage

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China Communications 2016年第9期13卷 203-207页

作者： Pingzhi Fan Southwest Jiaotong University ChengduChina

To cope with the impact of big data,it is proposed to integrate the traditionally individual computing,communications and storage systems,which are getting inevitably *** effective information system capacity is introduced and discussed,aimed at excavating potentials of information system under a new paradigm with more degrees of freedom.

关键词： big data communications com- puting storage information system capacity

Extracting Integrated Features of Electronic Medical Records big data for Mortality and Phenotype Prediction

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Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG Institute of Computing Technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance *** paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale *** processing uses two variational auto-encoders(VAEs) to automatically extract individual and global *** avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance *** conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing *** results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： databases Time series analysis MIMICs Manuals big data Feature extraction Transformers

A Systematic Review Towards big data Analytics in Social Media

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big data Mining and Analytics 2022年第3期5卷 228-244页

作者： Md.Saifur Rahman Hassan Reza School of Electrical Engineering and Computer Science University of North DakotaGrand ForksND 58202USA

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 *** new era allows the consumer to directly connect with other individuals,business corporations,and the *** are open to sharing opinions,views,and ideas on any topic in different formats out *** creates the opportunity to make the"big Social data"handy by implementing machine learning approaches and social data *** study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data *** explain why social media data are significant elements of the improved data-driven decision-making *** propose and build the"Sunflower Model of big data"to define big data and bring it up to date with technology by combining 5 V’s and 10 *** discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to *** create a taxonomy on social media analytics to meet the need and provide a clear ***,techniques,and supporting data type are also discussed in this research *** a result,researchers will have an easier time deciding which social data analytics would best suit their needs.

关键词： big data social media big data analytics social media analytics text analytics image analytics audio analytics video analytics predictive analytics descriptive analytics prescriptive analytics diagnostic analytics

Association discovery and outlier detection of air pollution emissions from industrial enterprises driven by big data

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data Intelligence 2023年第2期5卷 438-456页

作者： Zhen Peng Yunxiao Zhang Yunchong Wang Tianle Tang China University of Geosciences Beijing School of Economics and ManagementBeijing 100038China Beijing Institute of Petrochemical Technology School of Economics and ManagementBeijing 102617China

Air pollution is a major issue related to national economy and people's *** present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro *** by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll *** by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on *** results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.

关键词： Air Pollution Emissions of Enterprises Outlier detection based on clustering Association Rule Mining Grey Association Analysis big data

data science for oceanography:from small data to big data

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big Earth data 2022年第2期6卷 236-250页

作者： Chengcheng Qian Baoxiang Huang Xueqing Yang Ge Chen Department of Information and Files North China Sea Marine Forecasting Center of State Oceanic AdministrationQingdaoChina College of Computer Science and Technology Qingdao UniversityQingdaoChina Marine Division 1 Piesat Information Technology Co.LtdBeijingChina College of Information Science and Engineering Ocean University of ChinaQingdaoChina Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina

The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being *** on the types and distribution of oceanographic data,this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean *** ocean science has not fully entered the era of big *** are two ways to expand the amount of oceanographic data to better understanding and man-agement of the *** the data level,fully exploit the potential value of big and small ocean data,and transform the limited,small data into rich,big data,will help to achieve *** the application level,oceanographic data are of great value if realize the federation of the core data owners and the *** oceanographic data will provide not only a reliable scientific basis for climate,ecological,disaster and other scientific research,but also provide an unprecedented rich source of information that can be used to make predictions of the future.

关键词： Ocean science data big data small data