Transforming Industries with Big Data-Powered AI: Case Studies and Insights (Published)
This research paper explores the pivotal role of Big Data in advancing the field of Artificial Intelligence (AI) and delves into the opportunities, challenges, and wider implications associated with this intersection. As AI continues to evolve, the demand for massive datasets and sophisticated algorithms has grown exponentially. Big Data, characterized by its volume, velocity, variety, and veracity, provides a rich source of information to enhance AI capabilities. The paper begins by elucidating the fundamental connection between Big Data and AI, highlighting how large-scale datasets are essential for training, validating, and improving AI models. It underscores the significance of structured and unstructured data from diverse sources, including social media, IoT devices, and healthcare records, in shaping AI’s learning and decision-making processes. The paper also explores the potential for AI to revolutionize data analysis, enabling the discovery of hidden insights, patterns, and correlations that were previously unattainable.
Keywords: Artificial Intelligence, Big Data, General Data Protection Regulation, Gradient Boosting Algorithms, machine learning
Adaptive Query Processing in Big Data Workloads: Learning from Data (Published)
In the era of big data, the efficient processing of complex and resource-intensive queries has become a critical challenge. Traditional query optimization techniques often fall short of providing satisfactory performance when dealing with massive datasets and complex query workloads. To address these issues, this paper explores the concept of adaptive query processing, wherein query optimization strategies are dynamically adjusted based on insights gained from the data itself. We present a comprehensive study of adaptive query processing techniques tailored to big data workloads. Through the analysis of real-world big data scenarios, we examine the limitations of conventional query optimization methods and highlight the need for more flexible and data-driven approaches. Our research focuses on leveraging machine learning and statistical analysis to adapt query optimization strategies on the fly. This paper also discusses practical implementations of adaptive query processing within popular big data platforms and databases, showcasing real-world performance improvements achieved through these adaptive strategies. This abstract outlines the key points and objectives of a hypothetical research paper on adaptive query processing in the context of big data workloads, emphasizing the importance of learning from data to optimize query performance. The actual content and findings of the paper will be elaborated upon in the full paper.
Keywords: Adaptive Query Processing, Big Data Workloads, Data-Driven, Database Optimization, Query Performance, Query Planning, Statistical Analysis, machine learning, query optimization