Optimization of Dynamic Analytical Queries Using Advanced Methods

Abstract

In modern data management systems, redundant computations and data duplication are the current challenges. This occurs because of analytical queries which often share common computational components called common subexpressions, significantly increasing processing time and costs. This phenomenon is present in various data processing paradigms, such as transactional (OLTP), analytical (OLAP), and real time processing (RTAP) systems.Recent studies demonstrated that over 18% of queries in cloud environments, such as Alibaba, contain computational duplication. Two queries are considered duplicates if they share the same subexpressions or have structural similarities. The challenge of discovering and exploiting these subexpressions is known as the common subexpression selection problem, a complex NP-complete problem.This problem is relevant to several key areas of database performance optimization, including multiquery optimization, materialized views, query rewriting, and big data and cloud computing.Physical scene selection is a key solution in this field, aiming to choose the best set of subexpressions that achieve performance optimization while considering storage, update, and reuse constraints. Although this area has been studied since the 1990s through more than 180 research papers, most traditional solutions remain limited to static environments and small query sizes.Today, however, modern applications require dynamic and large-scale processing. Systems like BIGSUBS[81] have proved their ability to detect more then thousands of subexpressions, reducing execution time and cost. Therefore, developing smarter and more flexible solutions has become essential. This thesis aims to propose solutions based on deep learning, using efficient data structures such as graphs, with costbased models to estimate the benefit of common expression selection. The goal is to build scalable optimization strategies compatible with modern and complex query processing systems.