Next-Generation Query Optimization: A Systematic Review of Cost Models, Equivalence Rules, and Materialized Views

Query optimization is a crucial aspect of modern database management, relying heavily on cost models, equivalence rules, and materialized views to achieve efficient performance. This systematic review aims to critically evaluate the strengths and weaknesses of these three optimization levers, highlighting their implications for relational databases and distributed frameworks such as Hadoop, Spark, and Cloud SQL engines. A systematic review protocol was adopted across IEEE Xplore, ACM Digital Library, Springer, and Scopus. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guided the process to ensure transparency and replicability. A total of 5,908 records were identified and through the inclusion and exclusion criteria of the study, 7 studies were eventually retained for synthesis. Data were extracted systematically, recording author, year, database context, optimization technique, and main findings. The evidence indicates that adaptive cost models achieve 15–30% efficiency gains over classical models but at higher computational cost, graphbased equivalence transformations reduce query latency by around 18% compared to static rules, and materialized views deliver 15–32% performance gains but introduce 10–20% storage overhead and refresh indices between 3.4 and 4.0. Relational systems showed 72–80% efficiency but limited scalability, while distributed platforms achieved 82–88% efficiency with scalability scores between 3.8 and 4.5/5, though accompanied by energy and refresh penalties.