Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud

Abstract

This comprehensive overview explores the integration of machine learning (ML) in data warehousing, focusing on optimization challenges, methodologies, results, and future trends. Data warehouses, central to reporting and analysis, undergo a transformative shift with ML, addressing challenges like high maintenance costs and failure rates. The integration enhances performance through query optimization, indexing, and automated data management. Results showcase ML's application in predictive analytics for workload management, automated query optimization, and adaptive resource allocation, thus improving efficiency. However, challenges include data privacy, security concerns, and skill/resource constraints. The future scope anticipates trends like Explainable AI, Automated ML, Augmented Analytics, Federated Learning, and Continuous Intelligence, offering potential impacts on decision-making, resource allocation, data management, privacy, and real-time responsiveness. This succinct summary encapsulates the critical aspects of ML in data warehousing for holistic understanding.