Big Data Platform Research Articles

Understanding and prioritising cyberattack paths amid growing organisational complexity

This paper explores the role of attack path modelling (APM) in modern cyber security, addressing the challenges posed by the rapidly evolving digital landscape. It provides a comprehensive overview of APM frameworks and their application in identifying and prioritising potential attack paths. The challenges associated with manual APM efforts, the need for standardisation and the potential for innovation in automated APM tools are examined throughout. Drawing from real-world examples, the paper demonstrates the practical implications of APM in dissecting attack components and mitigating risks. It emphasises the dual approach of human-led APM initiatives and the integration of APM functionality in technical solutions, advocating for improved hygiene with manual and periodic APM assessments that can be optimised with advanced SecOps APM tooling. The paper serves as a general resource for all cyber security practitioners, providing insights into the historical context, frameworks and practical challenges of APM. The paper describes the significance of human-led APM initiatives, using open frameworks to enhance cyber security posture. Furthermore, the paper explores the evolving landscape of APM tools, anticipating their integration with big data platforms and artificial intelligence (AI) for comprehensive security analyses. This paper presents insights into the current state of cyber security, the practical applications of APM frameworks, and the potential future developments in APM technology.

The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning

In modern society, people's pace of life is fast, and the pressure is enormous, leading to increasingly prominent issues such as obesity and sub-health. Traditional fitness methods cannot meet people's needs to a certain extent. Therefore, this work aims to use technology to change people's lifestyles and compensate for traditional fitness methods' shortcomings. Firstly, this work overviews neurorobotics, providing neural perception and control functions for aerobics intelligent fitness system. Secondly, the connection between big data and machine learning (ML), big data technology products, and the ML process are discussed. The Spark big data platform builds node data for calculation, and the decision tree algorithm is used for data preprocessing. These are important for future intelligent fitness analysis. This work proposes an aerobics intelligent fitness system based on neurorobotics technology and big data analysis and develops a recommendation system for the best fitness exercise. This system utilizes neural perception and control functions, combined with big data and ML technology, to solve the obesity and sub-health problems faced by people in fast-paced and high-pressure lifestyles. By harnessing the computational capabilities of the Spark big data platform and applying the decision tree algorithm for data preprocessing, the system can furnish users with personalized fitness plans and optimization recommendations. This work conducts a model performance study on 35 % aerobic fitness data on intelligent fitness Android v1.0.8 to evaluate the system's data processing ability and training effectiveness. Moreover, the aerobics intelligent fitness system models based on neurorobotics, big data, and ML are evaluated. The results indicate that normalizing the data using the Min-Max method leads to a decrease in the F1 value and a reduction in data set errors. Consequently, the dataset studied by the system model is beneficial to improving the work efficiency of the aerobics intelligent fitness system. After the comprehensive human quality of the system model is evaluated, the actual average score of the comprehensive human quality of the 13 users tested before the aerobics intelligent fitness system test is 91.44, and the average prediction score is 90.88. The results of the two tests are similar. Thus, using the intelligent fitness system can enable the user to obtain system feedback according to the actual training effect, thereby playing a guiding role in the intelligent fitness of aerobics for the user. This work designs and implements the aerobics intelligent fitness system close to the human body's training effect, further enhancing the specialization and individualization of sports and fitness.

Empowering the Relationship Between the Government Functional Departments and Local Government Integration: An Integrated Study Based on Urban Digital Governance Platforms

Within the context of modernising government governance, the "platform" in the construction of digital government has become key in driving the efficiency of government services and management. This paper, based on the Technology-Organisation-Environment (TOE) framework and combined with theories of holistic governance and collaborative governance, delves into the crucial role of information technology and emerging governance models in eliminating barriers between government departments, optimising departmental structures, and fostering mechanisms for inter-departmental collaboration and sharing. Throughout the research, emphasis is placed on the importance of emerging technologies such as cloud computing, big data, and artificial intelligence in promoting efficient collaboration and resource sharing between government departments. By integrating and optimising governmental internal and inter-departmental data resources and service processes, it is possible to effectively advance the enhancement and integration of the relationship between the government functional departments and local government relations, adapt to the rapid changes in societal demands, and provide more precise and convenient public services. The core objective of this study is to establish a unified government big data platform, promoting the optimisation and integration of departmental structures, constructing an efficient mechanism for departmental collaboration and sharing, achieving data sharing and flow, and establishing a tight governance network. This aims to comprehensively enhance government governance capabilities and service quality, provide assurance for achieving more efficient and equitable governance objectives.

Empowering the Relationship Between the Government Functional Departments and Local Government Integration: An Integrated Study Based on Urban Digital Governance Platforms

Within the context of modernising government governance, the "platform" in the construction of digital government has become key in driving the efficiency of government services and management. This paper, based on the Technology-Organisation-Environment (TOE) framework and combined with theories of holistic governance and collaborative governance, delves into the crucial role of information technology and emerging governance models in eliminating barriers between government departments, optimising departmental structures, and fostering mechanisms for inter-departmental collaboration and sharing. Throughout the research, emphasis is placed on the importance of emerging technologies such as cloud computing, big data, and artificial intelligence in promoting efficient collaboration and resource sharing between government departments. By integrating and optimising governmental internal and inter-departmental data resources and service processes, it is possible to effectively advance the enhancement and integration of the relationship between the government functional departments and local government relations, adapt to the rapid changes in societal demands, and provide more precise and convenient public services. The core objective of this study is to establish a unified government big data platform, promoting the optimisation and integration of departmental structures, constructing an efficient mechanism for departmental collaboration and sharing, achieving data sharing and flow, and establishing a tight governance network. This aims to comprehensively enhance government governance capabilities and service quality, provide assurance for achieving more efficient and equitable governance objectives.

Big data processing and analysis platform based on deep neural network model

Users are increasingly turning to big data processing systems to extract valuable information from massive datasets as the field of big data grows. Data analytics platforms are used by e-commerce enterprises to improve product suggestions and model business processes. In order to meet the needs of large-scale data center operation and maintenance management, Internet companies often use Flink to process log data. This paper takes the big data processing and analysis platforms built by Internet financial companies and large banks as examples, and implants a stock prediction model based on Deep Neural Network (DNN). In this context, this paper completes the following work: 1) The research status of big data processing and analysis platforms at home and abroad is introduced. 2) Drawing on the modular design idea, the commercial bank big data platform is designed and the functions of each sub-module are introduced. Then the basic principle and structure of Convolutional Neural Networks (CNN) are expounded. 3) The optimal parameters of Convolutional Neural Networks are selected through experiments, and then the trained model is used for experiments. It can be seen that the stock prediction model proposed in this article has a higher prediction accuracy compared to existing models, which also verifies the validity of the proposed model. Input the data and compare the obtained results with the actual results, and finally show that the model in this paper has a good performance on stock prediction.

The role of grid management in community risk governance: a case study in Yuelu, China.

In this study, we aim to provide a comprehensive analysis of the effectiveness of the risk prevention and control mechanism within the grid management model for community risk prevention. We emphasize the importance of thoroughly examining the risk prevention and control mechanism to enhance risk management efforts in urban communities, particularly in response to unforeseen outbreaks such as COVID-19. Case studies are widely acknowledged as one of the most effective approaches to examine governance in China. In this study, the "Yuelu Model" serves as an illustrative example to demonstrate the application and effectiveness of grid management in community risk governance. To ensure the validity of the case study, it is imperative to adhere to the principle of representativeness. The collection of case data involves a combination of primary and secondary sources, and supplementary information is obtained through follow-up investigations conducted via WeChat, telephone, and other means, thereby enhancing the comprehensiveness and accuracy of the data. Our analysis reveals significant findings regarding the impact of the grid management model, fulfilling a triple role as a "Social Safety Valve" in the management process: (1) Community stress reduction function, (2) Community alarm function, and (3) Community integration function. Furthermore, we explore the adaptability of the grid management mechanism in addressing community risks, highlighting its effectiveness and potential for broader application. The findings of this study suggest that: Firstly, it is crucial to establish a shared information repository among different departments on a big data platform. Secondly, a dynamic government public information internal network should be established through collaborative efforts among multiple departments. Thirdly, implementing a regular (or periodic) early warning mechanism is essential. Lastly, the establishment of a high-quality talent team for power grid management is highly recommended. Our research provides valuable insights to enhance community risk governance.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

Abstract. This study addresses the intricate challenges encountered in the data governance process of Non-grain Production (NGP) on Arable land. This involves managing data from diverse sources, with varying accuracies and formats, and utilizing multiple specialized software tools. An object-oriented approach is adopted to encapsulate experiential knowledge related to the data and associated processing methods, thus creating an Application Knowledge Body Model (AKBM). This model acts as a conduit between users and computational resources, encompassing various types of data and their corresponding processing and analysis methods. Moreover, by employing model inference techniques to devise methods for transitioning from raw data models to target models, a foundation is laid for the accumulation, sharing, and intelligent application of expertise on data, methods, models, and knowledge.The application examples demonstrate that users can directly construct new solutions containing relevant data and associated processing methods, rather than grappling with a multitude of data files and complex specialized software when encountering novel challenges. This promotes collaborative development in data governance on geospatial big data platforms, significantly enhancing governance efficiency, improving the quality of information support in NGP cultivation management, advancing current technological capabilities, and fostering the progression of related technologies.

Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing

Abstract. The Chlorophyll-a (Chla) concentration is an important parameter characterizing the water quality of rivers and lakes. Satellite remote sensing provides new opportunities for the quantitative monitoring of Chla concentration in large-scale water bodies. In this study, Sentinel-2 satellite remote sensing data integrated on the Google Earth Engine (GEE) remote sensing big data platform are employed, along with hourly measured water quality site data, to establish a quantitative inversion model for Chla concentration water quality parameters in Jiujiang City within Jiangxi Province, China. The Chla concentration is estimated for each quarter from 2020 to 2022, and the spatial distribution is analyzed, revealing the changing trend of Chla concentration over the past three years. The key findings are as follows: (1) The quantitative inversion model for Chla concentration has been validated with measured data, achieving a model accuracy of 0.53; (2) The spatial inversion results of Chla concentration exhibit an increasing trend that is consistent with actual measurement site results; (3) Owing to the influence of human activities and the peak-low water level of rivers and lakes, Chla concentration shows a discernible seasonal variation pattern. This methodology offers a new perspective for analyzing the seasonal variation characteristics of Chla concentration in rivers and lakes, providing valuable insights for the sustainable management of river and lake water quality.

Digital media and interactive E-learning application in art teaching process based on big data platform

Digital media art integrates multiple fields such as art teaching, computer graphics, and AI. With the application of new computer technologies such as artificial intelligence, serving art education through network technology has become increasingly popular. This article studies the application of digital media and interactive E-learning based on big data platforms in the process of art teaching.It can facilitate the sharing of educational resources, enable collaborative learning, and support the development of new educational models that are more efficient and effective. By analyzing the needs of students for mobile learning and personalization, this article adopts data storage algorithms and their improved algorithms as a method to improve the electronic learning system, thereby achieving the digital and networked development of art education and providing a new model for the e-learning teaching mode of art courses.

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.

Establishing a Big Data Driver for Digital Transformation, Supplier Concentration and Total Factor Productivity in Enterprises

<>Nowadays, various industries are increasingly investing in constructing big data platforms to achieve digitalisation. Digitalisation has become a critical factor in improving the efficiency and effectiveness of enterprises. This paper explores the influence of digital transformation (DIT) on corporate total factor productivity (TFP). Based on data from publicly listed enterprises in China from 2007 to 2022, this paper demonstrates that DIT significantly improves their TFP. Supplier concentration partially mediates the relationship between DIT and TFP. Furthermore, the enterprise life cycle moderates the impact of DIT on TFP. Specifically, during the growth stage, the influence of DIT on TFP is not significant. During the maturity and decline stages, DIT positively affects the TFP. The impact of DIT on TFP is particularly pronounced in enterprises in western China, as well as in large and high-tech enterprises. The paper examines whether, how and when DIT affect TFP. The findings contribute novel evidence that strategic DIT enable enterprise to improve TFP through decreased supplier concentration. For both scholars and practitioners, this research provides valuable insights into how digital transformation can pay dividends by improving overall productivity.<>

Does digital transformation benefit innovation in manufacturing firms? Empirical evidence based on data mining and textual analysis methods

With the increasing spread of the digital economy and the application of digital technologies, more and more manufacturing enterprises have begun to improve the efficiency of digital transformation with the help of big data and other digital technologies to promote the high-quality development of enterprises. Through the open big data platform, manufacturing enterprises can collect data and information reflecting the needs of users in a timely manner to achieve real-time optimization of the production process and timely detection of problems to a certain extent, thereby improving the ability to research and innovation. Through the establishment of econometric models and empirical tests, we also find that the education level of managers affects the process of digital transformation and influences the impact of digital transformation on innovation.

The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence

With the rapid development of artificial intelligence technology, the function of artificial intelligence to help the high-quality development of sports is increasingly visible, and the continuous, comprehensive and deep integration of artificial intelligence and sports will be an inevitable trend. This paper summarized and analyzed the characteristics of the tennis research theme literature published in the core journals of China from 2012 to 2022 by using the methods of literature and logical induction. The results show that, in the past 10 years, there are problems such as insufficient use of artificial intelligence in technical and tactical statistical analysis, insufficient use in tennis professionalization research, lack of artificial intelligence perspectives in tennis tournament research, and shortage of artificial intelligence use in tennis psychology research. In the future, scientific research on tennis in China should rely on scientific and technological means such as 5G technology, big data technology, computer vision and intelligent wearable devices. etc, cross integrate multidisciplinary knowledge, accurately locate the physical fitness weaknesses of Chinese tennis players, focus on building an intelligent measurement system for tennis players, and scientifically develop a big data platform for tennis skills and tactics; And guided by practical problems, we should focus on exploring intelligent wearable devices suitable for tennis sports programs, and integrating computer vision technology with intelligent wearable devices to achieve complementary advantages and enhance the accuracy and credibility of data collection. And use natural science methods to conduct quantitative analysis to provide scientific solutions for the development of tennis.

Database Access Information Security Management Simulation under Big Data Platform

When people perform database access information security management, the traditional method cannot accurately verify the identity of the visitor, the credibility of the identity information, and the security management of the access information. With the widespread application of big data technology, the amount of data in databases is rapidly increasing, which brings new challenges to information security management. The main purpose of this study is to explore how to more effectively manage the security of database access information on big data platforms. Therefore, the trusted computing platform is established to implement database access information security management under the data platform. The method determines the user behavior is credible by establishing a behavior chain of behavior based on the user identity and measuring user operation behavior. For the user's private data, the encryption/decryption module is used for security protection, preventing data from being leaked through illegal copying. A trusted metric model based on the USB Key user identity is established and a trusted platform is established. By improving the ELGamal algorithm, the IMC/IMV metrics architecture is utilized to measure platform security attributes. In the first round of anonymous authentication, the identity authentication of the platform is completely completed, and the database access information security management under the big data platform is completed. The simulation results show that in 10 experiments, the transmission time delay of TCP/IP protocol is less than 200ms, and the security of database access information is enhanced after the encryption system is established in the database. This has certain theoretical enlightenment for the improvement of database security and the optimization of information security management.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Electric Energy Metering Error Evaluation Method Based on Deep Learning

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified.

A Basketball Big Data Platform for Box Score and Play-by-Play Data.

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

Evaluation and analysis of water-regulation value of ecosystem in Qinghai area of Qilian Mountain National Park, China

Context Water-regulation services significantly affect local social and economic development in the Qinghai area of Qilian Mountain National Park, China, and also affect the role of a ‘solid reservoir’ in China. Aim The value and change trend of water regulation was assessed through the evaluation and prediction of the water-regulation value of ecosystems. Methods InVEST water yield model and SVR model are used to evaluate and predict the water-regulation value of ecosystem in this study. Key results Grassland plays an important role in water regulation, although the area of forestland is not large, its water-regulation monetary value in 2020 accounted for 13.92% of the total evaluation value. From 2010 to 2020, the physical value of water regulation increased by 2.24% and the monetary value increased ~0.97% annually. In 2030, the amount of physical and monetary value of water regulation will be increased. Conclusion Grassland plays an important role in water regulation. It is necessary to strengthen the asset management of water regulation and optimise the ecological resources. Implications The study findings are utilised to enhance monitoring and establish ecological big-data platform, thereby elevating the quality of national park development management and decision-making services.

Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics

Sophisticated cyber-infrastructure and information technology methods are necessary to exploit and analyse the massive amounts of data generated by online transactions. This study introduces a big data platform for online retailers to tackle various issues in the e-commerce industry. Both people and businesses are vulnerable to fraud, which is a worldwide problem. In today's tech-driven society, the battle against fraud has been greatly aided by machine learning (ML) and artificial intelligence (AI). This essay takes a look at the conventional wisdom about fraud prevention and shows how outdated it is when compared to modern fraud techniques. It delves further into the ways in which ML and AI are supporting fast digitization, which in turn revolutionises fraud prevention efforts. Machine learning and artificial intelligence algorithms enable companies to comb through massive amounts of data for patterns and anomalies that could suggest fraudulent activity. In this article, we will explore how machine learning and artificial intelligence may greatly enhance fraud prevention efforts. These technologies can help with advanced data analytics, anomaly detection, and predictive modelling. The text highlights the ways in which these technologies empower organisations to proactively identify and reduce fraud risks, protecting both their operations and stakeholders.

Integrated Two-Level New Energy Monitoring and Big Data Centers: A Comprehensive Approach for Urban Sustainability

With the help of the quick development of big data, the Internet of Things, and other related technologies, the construction of new energy monitoring and big data centres aims to enhance the control capability of urban new energy stations. Inefficient operation and maintenance of new energy stations, as well as chaotic production control, are the reasons behind this action. Given the limitations of the current stage of new energy big data platform construction, the construction of group-level new energy monitoring and big data centres to achieve a high degree of integration and sharing of resources and experience has become an inevitable development of the new energy industry. This project aims to achieve multi-level integration in decision-making from the bottom up by using the background of the construction of the Wenzhou New Energy Two-Level Centre as a guide, organising the business processes for new energy production and equipment control, designing the technical architecture of the platform for cloud-side collaboration, providing guidance for the heterogeneous data processing and analysis of new energy assets in the cloud, and acting as a model for the development of the group-level intelligent platform for the new energy industry.