AI-Enhanced Data Governance for Modernizing the US Court System (Published)
The US court system is currently burdened by inefficiencies, data silos, and security vulnerabilities that urgently require modernization to restore public trust. Outdated legacy systems, fragmented data practices, and limited interoperability hinder case management and transparency. A robust data governance framework powered by cutting-edge technologies like Artificial Intelligence (AI), blockchain, and federated learning is essential to address these pressing challenges. This paper explores how AI-enhanced data governance can swiftly transform the judicial system by ensuring data integrity, security, and accessibility. It presents solutions that modernize the court system and offer scalable applications for other sectors, such as healthcare, finance, and education. Adopting centralized data platforms, AI-driven data management, and advanced encryption methods can enhance operational efficiency, reduce biases, and improve decision-making processes. By leveraging this technology-driven framework, the judiciary can deliver justice more effectively, regain public trust, and set a precedent for modernization across industries.
Keywords: AI-enhanced data governance, Operational Efficiency, Predictive Analytics, US court modernization, blockchain security, centralized data platforms, data integrity, federated learning, judicial transparency, privacy compliance
Machine Learning for Query Processing in Big Data Analytics: Trends (Published)
As the era of big data continues to evolve, the need for efficient and effective query processing in big data analytics has become paramount. Traditional query processing methods often struggle to cope with the sheer volume, velocity, and variety of data generated in today’s data-driven world. To address these challenges, machine learning techniques have emerged as a promising avenue to enhance query processing in big data analytics. This abstract provides an overview of the key trends in utilizing machine learning for query processing in the realm of big data analytics. It explores the various ways in which machine learning is transforming the field, from query optimization and performance enhancement to natural language query understanding and automated data discovery. The trends discussed in this abstract include: Query Optimization, Predictive Analytics, Natural Language Processing (NLP), Automated Data Discovery, and Data Quality Improvement, This abstract highlights the growing importance of machine learning in the domain of big data analytics and offers insights into how these trends are shaping the future of query processing. Machine learning is a driving force behind the evolution of big data analytics, enabling organizations to extract meaningful insights and value from their vast data repositories.
Keywords: Automated Data Discovery, Big Data Analytics Trends, Natural Language Processing (NLP), Predictive Analytics, Query Processing, machine learning, query optimization