Algorithms for Efficient Top-Down Join Enumeration

Inhaltsverzeichnis (Table of Contents)

• Zusammenfassung
• Abstract
• 1 Einleitung
• 2 Grundlagen
• 2.1 Relationale Algebra
• 2.2 Anfrageoptimierung
• 2.3 Top-Down Join Enumeration
• 2.4 Branch-and-Bound Pruning
• 3 Algorithmen für Top-Down Join Enumeration
• 3.1 Partitionierung von Graphen
• 3.1.1 Der Greedy-Algorithmus
• 3.1.2 Der Greedy-Algorithmus mit dynamischer Partitionierung
• 3.2 Erweiterung der Partitionierungsstrategien
• 3.2.1 Der Greedy-Algorithmus für komplexe Join-Prädikate
• 3.2.2 Ein generisches Framework für Top-Down Join Enumeration
• 4 Optimierung der Branch-and-Bound Pruning-Strategien
• 4.1 Branch-and-Bound Pruning-Strategien
• 4.1.1 Kosten-basierte Pruning
• 4.1.2 Kardinalitäts-basierte Pruning
• 4.2 Verbesserungen der Branch-and-Bound Pruning-Strategien
• 4.2.1 Kombination von Pruning-Strategien
• 4.2.2 Pruning im Greedy-Algorithmus
• 4.2.3 Optimierungen der Kosten-basierten Pruning-Strategie
• 4.2.4 Optimierungen der Kardinalitäts-basierten Pruning-Strategie
• 5 Experimentelle Evaluierung
• 5.1 Evaluierung der Partitionierungsalgorithmen
• 5.2 Evaluierung der Branch-and-Bound Pruning-Strategien
• 6 Zusammenfassung und Ausblick

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

The primary objective of this dissertation is to improve the efficiency of top-down join enumeration in query optimization for database management systems. This is achieved by developing novel algorithms and enhancing existing methods for graph partitioning and branch-and-bound pruning.

• Efficient algorithms for graph partitioning in the context of top-down join enumeration
• Handling complex join predicates and outer joins in top-down join enumeration
• Optimization and enhancement of existing branch-and-bound pruning strategies
• Experimental evaluation of the proposed algorithms and strategies using various benchmarks
• Analysis and comparison of the performance improvements achieved through the proposed methods

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Einleitung This chapter introduces the topic of query optimization and its importance for database management systems. It highlights the limitations of existing methods for top-down join enumeration, particularly in handling complex join predicates and outer joins. It also introduces the objectives and key themes of the dissertation.
• Chapter 2: Grundlagen This chapter provides essential background information on relational algebra, query optimization, top-down join enumeration, and branch-and-bound pruning. It lays the foundation for the subsequent chapters by defining key concepts and terminology.
• Chapter 3: Algorithmen für Top-Down Join Enumeration This chapter presents two efficient graph-partitioning algorithms designed for top-down join enumeration. It then extends these algorithms to handle complex join predicates and outer joins, introducing a generic framework for top-down join enumeration.
• Chapter 4: Optimierung der Branch-and-Bound Pruning-Strategien This chapter explores existing branch-and-bound pruning strategies and proposes improvements to their combination. It presents optimizations for both cost-based and cardinality-based pruning techniques, aiming to enhance their effectiveness, robustness, and efficiency.
• Chapter 5: Experimentelle Evaluierung This chapter presents experimental results evaluating the performance of the proposed algorithms and strategies. It uses benchmark datasets like TPC-H, TPC-DS, and SQLite to measure the efficiency and effectiveness of the developed methods, showing significant improvements in query optimization performance.

Schlüsselwörter (Keywords)

This dissertation focuses on query optimization, specifically on improving the efficiency of top-down join enumeration. Key areas of research include graph partitioning, complex join predicates, outer joins, branch-and-bound pruning, and experimental evaluation of performance improvements. The work contributes to advancements in the area of database management systems, particularly in the optimization of query processing.