Optimizing Data Warehousing Performance Through Machine Learning Algorithms in the Cloud

EasyChair Preprint no. 11679

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.

Keyphrases: algorithm, Cloud, Data Warehousing, machine learning

@Booklet{EasyChair:11679,
  author = {Sina Ahmadi},
  title = {Optimizing Data Warehousing Performance Through Machine Learning Algorithms in the Cloud},
  howpublished = {EasyChair Preprint no. 11679},

  year = {EasyChair, 2024}}