Digital Government Information Platform Construction: Technology, Challenges and Prospects

As big data continues to proliferate at an unprecedented rate, cloud computing has emerged as a fundamental technology for managing, storing, and processing these vast datasets, with global data volumes projected to reach 175 zettabytes by 2025. Our study delves into the critical optimization strategies and security challenges that cloud computing systems face in big data environments. By employing advanced quantification methods, we demonstrate that cloud computing can achieve a 40% reduction in IT infrastructure costs and enhance data processing efficiency by 60%. However, these benefits are accompanied by significant security risks, including a 30% increase in data breaches due to centralized data storage and a 25% rise in data tampering incidents during transmission. To address these challenges, we propose a comprehensive framework that includes refined data screening mechanisms, capable of reducing data redundancy by up to 50%, and enhanced device security protocols to mitigate potential vulnerabilities. Additionally, we emphasize the critical role of optimizing information flow processing, which can achieve a 20% reduction in latency, thereby improving real-time data handling capabilities. Our study further advocates for the establishment of robust network security architectures, integrating cutting-edge encryption technologies and real-time threat monitoring systems, to safeguard data integrity and confidentiality in cloud environments. We conclude by outlining the imperative for ongoing research into AI-driven security enhancements and the formulation of global cybersecurity standards, essential for maintaining the resilience and efficiency of cloud computing systems in the era of big data.

Optimization and Security Challenges in Cloud Computing within Big Data Enviro

Lin

2024

Leveraging Blockchain Empowered Machine Learning Architectures for Advanced Financial Risk Mitigation and Anomaly Detection

2024

AI-powered Strategies for Optimizing Waste Management in Smart Cities in Beijing

Yao,

Weng,

et al. 2024

The study investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into Beijing's waste management system, emphasizing their effects on operational efficiency, environmental sustainability, and economic feasibility. The deployment of AI-driven route optimization and IoT-enabled real-time monitoring resulted in a 25% reduction in waste collection trips and a 30% decrease in waste overflow incidents. These advancements led to notable reductions in fuel consumption and environmental impact, while an economic analysis projected a Net Present Value (NPV) of $3.5 million over a 10-year period, affirming the financial benefits of these technologies. The findings highlight the pivotal role of AI and IoT in optimizing urban waste management practices. The study offers policy recommendations for the phased and strategic adoption of these technologies within Beijing, with the potential to enhance efficiency and contribute to the city’s sustainability objectives. Future research is advised to examine the long-term sustainability of AI-driven waste management strategies and assess the applicability of these technologies in diverse urban environments.