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Big spatial data for urban and environmental sustainability

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Geo-Spatial Information Science 2020年第2期23卷 125-140页

作者： Bo Huang Jionghua Wang Department of Geography and Resource Management The Chinese University of Hong KongHong KongChina

Eighty percent of Big data are associated with spatial information,and thus are Big Spatial Data(BSD).BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics.To fully leverage the advantages of BSD,it is integrated with conventional data(e.***.remote sensing images)and improved methods are developed.This paper introduces four case studies:(1)Detection of polycentric urban structures;(2)Evaluation of urban vibrancy;(3)Estimation of population exposure to PM2.5;and(4)Urban land-use classification via deep learning.The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD.Meanwhile,they can also improve the effectiveness of Big data itself.Finally,this study makes three key recommendations for the development of BSD with regards to data fusion,data and predicting analytics,and theoretical modeling.

关键词： Big spatial data analytics review spatial modeling data fusion

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 247-257页

作者： Jiajun Wang Tufan Kumbasar the School of Automation Hangzhou Dianzi University IEEE the Control and Automation Engineering Department Faculty of Electrical and Electronics EngineeringIstanbul Technical University

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,Big bang-Big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

关键词： Big bang-Big crunch (BBBC) interval type-2 fuzzy neural networks (IT2FNNs) parameter optimization particle swarm optimization (PSO)

Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: Big data analysis

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Neural Regeneration Research 2019年第6期14卷 1099-1104页

作者： Ulfuara Shefa Junyang Jung Department of Biomedical Science Graduate SchoolKyung Hee University Department of Anatomy and Neurobiology College of MedicineKyung Hee University

A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current Big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a Big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.

关键词： Schwann cells Big data analysis peripheral nerve degeneration peripheral nerve regeneration microarray matched genes promising genes gene ranking

Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools

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Big Data Mining and Analytics 2019年第1期2卷 48-57页

作者： Sunil Kumar Maninder Singh the Directorate of Livestock Farms Guru Angad Dev Veterinary and Animal Sciences University the Department of Computer Science Punjabi University

In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as Big data. The health industry sector has been confronted by the need to manage the Big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various Big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of Big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of Big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system.

关键词： Big data healthcare Hadoop MapReduce

Dynamic data auditing scheme for Big data storage

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Frontiers of Computer Science 2020年第1期14卷 219-229页

作者： Xingyue CHEN Tao SHANG Feng ZHANG Jianwei LIU Zhenyu GUAN School of Electronic and Information Engineering Beihang UniversityBeijing 100083China School of Cyber Science and Technology Beihang UniversityBeijing 100083China

When users store data in Big data platforms,the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data.However,the existing remote data auditing schemes for Big data platforms are only applicable to static data.In order to verify the integrity of dynamic data in a Hadoop Big data platform,we presents a dynamic auditing scheme meeting the special requirement of Hadoop.Concretely,a new data structure,namely Data Block Index Table,is designed to support dynamic data operations on HDFS(Hadoop distributed file system),including appending,inserting,deleting,and modifying.Then combined with the MapReduce framework,a dynamic auditing algorithm is designed to audit the data on HDFS concurrently.Analysis shows that the proposed scheme is secure enough to resist forge attack,replace attack and replay attack on Big data platform.It is also efficient in both computation and communication.

关键词： Big data data security remote data auditing dynamic update privacy protection

Optimal PID Control of Spatial Inverted Pendulum With Big Bang–Big Crunch Optimization

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 822-832页

作者： Jia-Jun Wang Tufan Kumbasar School of Automation Hangzhou Dianzi UniversityHangzhou 310018China IEEE Control and Automation Engineering Department Faculty of Electrical and Electronics EngineeringIstanbul Technical UniversityIstanbul TR-34469Turkey

As the extension of the linear inverted pendulum(LIP) and planar inverted pendulum(PIP), this paper proposes a novel spatial inverted pendulum(SIP). The SIP is the most general inverted pendulum(IP) than any existing IP. The model of the SIP is presented for the first time. The SIP inherits all the characteristics of the LIP and the PIP, which is a nonlinear,unstable and underactuated system. The SIP has five degrees of motion freedom and three control forces. Thus, it is a multipleinput and multiple-output(MIMO) system with nonlinear dynamics. To realize the spatial trajectory tracking of the SIP,the control structure with five PID controllers will be designed.The parameter tuning of the multiple PIDs is a challenging work for the proposed SIP model. To alleviate the difficulties of the parameter tuning for the multiple PID controllers, optimal PIDs can be achieved with the help of Big Bang – Big Crunch(BBBC) optimization. The BBBC algorithm can successfully optimize the parameters of the multiple PID controllers with high convergence speed. The optimization performance index of the BBBC algorithm is compared with that of the particle swarm optimization(PSO). Simulation results certify the rightness and effectiveness of the proposed control and optimization methods.

关键词： Big Bang–Big Crunch(BBBC) optimal PID control spatial inverted pendulum(SIP) spatial trajectory tracking

Development and Application of Big Data Platform for Garlic Industry Chain

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Computers, Materials & Continua 2019年第1期58卷 229-248页

作者： Weijie Chen Guo Feng Chao Zhang Pingzeng Liu Wanming Ren Ning Cao Jianrui Ding Shandong Agricultural University Tai’an271000China Shandong Provincial Agricultural Information Center Ji’nan250013China College of Information Engineering Qingdao Binhai UniversityQingdao266000China Department of Computer Science Utah State UniversityLoganUSA

In order to effectively solve the problems which affect the stable and healthy development of garlic industry,such as the uncertainty of the planting scale and production data,the influence factors of price fluctuation is difficult to be accurately analyzed,the difficult to predict the trend of price change,the uncertainty of the market concentration,and the difficulty of the short-term price prediction etc.the Big data platform of the garlic industry chain has been developed.Combined with a variety of data acquisition technology,the information collection of influencing factors for garlic industry chain is realized.Based on the construction of the Big data technology platform,the real-time synchronous acquisition,efficient storage and analysis of the planting,market,storage,processing,export and logistics information in five provinces and seven counties are realized.The application of the Big data platform for garlic industry chain has realized the accurate acquisition of garlic planting area,the price and trend of market circulation and the information of export information,analyzed the fluctuation regulation of garlic price,and also realized the short-term precision prediction of garlic price.

关键词： Garlic industry chain agricultural Big data planting area price fluctuation regulation price prediction

A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning

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Tsinghua Science and Technology 2019年第4期24卷 379-388页

作者： Yanxia Lv Sancheng Peng Ying Yuan Cong Wang Pengfei Yin Jiemin Liu Cuirong Wang School of Computer Science and Engineering Northeastern University Laboratory of Language Engineering and Computing and also with School of Cyber SecurityGuangdong University of Foreign Studies School of Information Science and Engineering Central South University

By combining multiple weak learners with concept drift in the classification of Big data stream learning, the ensemble learning can achieve better generalization performance than the single learning approach. In this paper,we present an efficient classifier using the online bagging ensemble method for Big data stream learning. In this classifier, we introduce an efficient online resampling mechanism on the training instances, and use a robust coding method based on error-correcting output codes. This is done in order to reduce the effects of correlations between the classifiers and increase the diversity of the ensemble. A dynamic updating model based on classification performance is adopted to reduce the unnecessary updating operations and improve the efficiency of learning.We implement a parallel version of EoBag, which runs faster than the serial version, and results indicate that the classification performance is almost the same as the serial one. Finally, we compare the performance of classification and the usage of resources with other state-of-the-art algorithms using the artificial and the actual data sets, respectively. Results show that the proposed algorithm can obtain better accuracy and more feasible usage of resources for the classification of Big data stream.

关键词： Big data stream classification online bagging ensemble learning concept drift

Introduction for the Special Issue on Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications

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Computer Modeling in Engineering & Sciences 2019年第5期119卷 245-247页

作者： Qianxin Wang Allison Kealy Shengjie Zhai School of Environment Science and Spatial Informatics China University of Mining and TechnologyXuzhou22116China School of Science Royal Melbourne Institute Technology UniversityMelbourne3001Australia Department of Mechanical Engineering University of NevadaLas VegasNevada89154USA

We live in the era of‘Big Data’.In particular,Geospatial data,whether captured through remote sensors(e.g.,satellite imagery)or generated from large-scale simulations(e.g.,climate change models)have always been significantly large in size.Over the last decade however,advances in instrumentation and computation has seen the volume,variety,velocity,and veracity of this data increase exponentially.Of the 2.5 quintillion(1018)bytes of data that are generated on a daily basis across the globe,a large portion(arguably as much as 80%)is found to be geo-referenced.Therefore,this special issue is dedicated to the innovative theories,methods,analytics,and applications of geospatial Big data.

关键词： Data innovative Big