Classification of Beijing Line 10 Subway Living Circle Based on Multi-source Big Data

In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.

Evaluation and Improvement Strategies for Slow Traffic Systems Based on Multi-source Big Data:A Case Study of Shijingshan District of Beijing City

The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.

A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

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 constraints.The 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.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.