Abstract
<jats:p>This volume explores evolving data warehousing and mining methodologies that support largescale analytical intelligence and evidence-based strategic decision-making. It highlights hybrid warehouse-lake architecture, automated data integration workflows, schema adaptation strategies, semantic data enrichment, and scalable analytical processing frameworks. The scope extends to advanced pattern recognition, clustering, predictive inference, and visualization-based analysis techniques designed to handle complex and multi-source datasets. Contributions focusing on performance tuning, metadata governance, workload-aware optimization, and quality assurance mechanisms are encouraged. Practical applications across business analytics, digital governance, medical informatics, financial intelligence, and industrial process optimization are particularly valuable. The volume aims to support the development of agile, interpretable, and context-aware analytical ecosystems that convert raw data into knowledgedriven insights</jats:p>