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.

The Influence of Social Media on College Choice among Undergraduates Majoring in Big Data Management and Application in China:Taking Guilin University of Electronic Technology as an Example

This study aims to investigate the influence of social media on college choice among undergraduates majoring in Big Data Management and Application in China.The study attempts to reveal how information on social media platforms such as Weibo,WeChat,and Zhihu influences the cognition and choice process of prospective students.By employing an online quantitative survey questionnaire,data were collected from the 2022 and 2023 classes of new students majoring in Big Data Management and Application at Guilin University of Electronic Technology.The aim was to evaluate the role of social media in their college choice process and understand the features and information that most attract prospective students.Social media has become a key factor influencing the college choice decision-making of undergraduates majoring in Big Data Management and Application in China.Students tend to obtain school information through social media platforms and use this information as an important reference in their decision-making process.Higher education institutions should strengthen their social media information dissemination,providing accurate,timely,and attractive information.It is also necessary to ensure effective management of social media platforms,maintain a positive reputation for the school on social media,and increase the interest and trust of prospective students.Simultaneously,educational decision-makers should consider incorporating social media analysis into their recruitment strategies to better attract new student enrollment.This study provides a new perspective for understanding higher education choice behavior in the digital age,particularly by revealing the importance of social media in the educational decision-making process.This has important practical and theoretical implications for higher education institutions,policymakers,and social media platform operators.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application,“Introduction to Big Data”has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone.To this end,we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning,course implementation,and course summary.After interviews with students and feedback from questionnaires,students are highly satisfied with some of the teaching measures and programs currently adopted.

Analysis on the Applicability of Meter Data Acquisition Methods Based on the Analytic Hierarchy Process

The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performance of each one by analytic hierarchy process. We can draw a conclusion by calculating＂ The local automatic meter reading, the prepaid electric energy metering and the remote automatic meter reading have almost the same performance. They are better than the manual meter reading and the vehicle mounted mobile automatic meter reading. So we can choose any one of the three. Among them, the prepaid electric energy metering performs best. This can be a reference for grid company＇ s decision.

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

Progress and Achievements of Fengyun Meteorological Satellite Program since 2022

Fengyun meteorological satellites have undergone a series of significant developments over the past 50 years.Two generations,four types,and 21 Fengyun satellites have been developed and launched,with 9 currently operational in orbit.The data obtained from Fengyun satellites is employed in a multitude of applications,including weather forecasting,meteorological disaster prevention and reduction,climate change,global environmental monitoring,and space weather.These data products and services are made available to the global community,resulting in tangible social and economic benefits.In 2023,two Fengyun meteorological satellites were successfully launched.This report presents an overview of the two recently launched Fengyun satellites and currently in orbit Fengyun satellites,including an evaluation of their remote sensing instruments since 2022.Additionally,it addresses the subject of Fengyun satellite data archiving,data services,application services,international cooperation,and supporting activities.Furthermore,the development prospects have been outlined.

Internet of Vehicles in Big Data Era

As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.

China Argo project:progress in China Argo ocean observations and data applications

This paper reviews the current achievements of the China Argo project. It considers aspects of both the construction of the Argo observing array, float technology, and the quality control and sharing of its data. The developments of associated data products and data applications for use in the fields of ocean, atmosphere, and climate research are discussed, particularly those related to tropical cyclones （typhoons）, ocean circulation, mesoscale eddies, turbulence, oceanic heat/salt storage and transportation, water masses, and operational oceanic/atmospheric/climatic forecasts and predictions. Finaliy, the challenges and opportunities involved in the long-term maintenance and sustained development of the China Argo ocean observation network are outlined. Discussion also focuses on the necessity for increasing the number of floats in the Indian Ocean and for expanding the regional Argo observation network in the South China Sea, together with the importance of promoting the use of Argo data by the maritime countries of Southeast Asia and India.

Improving Performance of Cloud Computing and Big Data Technologies and Applications

Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia

Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency(ESA) Global Land Cover Map(GlobCover), Landsat Thematic Mapper(TM), Enhanced Thematic Mapper(ETM), Multispectral Scanner(MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. Data evaluation show that the total user′s accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.

A Distributed In-Memory Database Solution for Mass Data Applications

In this paper, a Distributed In-Memory Database （DIMDB） system is proposed to improve processing efficiency in mass data applications. The system uses an enhanced language similar to Structured Query Language （SQL） with a key-value storage schema. The design goals of the DIMDB system is described and its system architecture is discussed. Operation flow and the enhanced SOL-like language are also discussed, and experimental results are used to test the validity of the system.

Rough-Set-Based Attribute Dependencies: Foundations and Bio-Medical Data Applications

Theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, finance, industry, multimedia, medicine, and most recently bioinformatics.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation： A case study in the Bahe River Basin of the Qinling Mountains, China

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.

An Overview of Smart City in China

The year of 2013 is considered the first year of smart city in China. With the development of informationization and urbanization in China, city diseases(traffic jam, medical problem and unbalanced education) are more and more apparent. Smart city is the key to solving these diseases. This paper presents the overall smart city development in China in term of market scale and development stages, the technology standards, and industry layout. The paper claims that the issues and challenges facing smart city development in China and proposes to make polices to support smart city development.

Intelligent decision support platform of new energy vehicles

New energy vehicles(NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability,the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

Research on the Development and Application of Campus Card in Colleges and Universities

With the rapid development of the construction of smart campus in Colleges and universities and the maturity of related technologies,campus card has become the most frequently used and the most frequently used core component of smart campus.Based on the actual construction of onecard system in domestic universities and the author’s years of experience in campus card management,this paper makes a systematic study on the development of campus card,virtual campus card,big data application,information security and other aspects,with a view to providing effective reference for the construction of campus card in Colleges and universities.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

BIG DATA:UNLEASHING INFORMATION

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.