Maintenance-cost view-selection in large data warehouse systems: algorithms, implementations and evaluations.

Abstract

Choi Chi Hon.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 120-126).Abstracts in English and Chinese.Abstract --- p.iAbstract (Chinese) --- p.iiAcknowledgement --- p.iiiContents --- p.ivList of Figures --- p.viiiList of Tables --- p.xChapter 1 --- Introduction --- p.1Chapter 1.1 --- Maintenance Cost View Selection Problem --- p.2Chapter 1.2 --- Previous Research Works --- p.3Chapter 1.3 --- Major Contributions --- p.4Chapter 1.4 --- Thesis Organization --- p.6Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Data Warehouse and OLAP Systems --- p.8Chapter 2.1.1 --- What Is Data Warehouse? --- p.8Chapter 2.1.2 --- What Is OLAP? --- p.10Chapter 2.1.3 --- Difference Between Operational Database Systems and OLAP --- p.10Chapter 2.1.4 --- Data Warehouse Architecture --- p.12Chapter 2.1.5 --- Multidimensional Data Model --- p.13Chapter 2.1.6 --- Star Schema and Snowflake Schema --- p.15Chapter 2.1.7 --- Data Cube --- p.17Chapter 2.1.8 --- ROLAP and MOLAP --- p.19Chapter 2.1.9 --- Query Optimization --- p.20Chapter 2.2 --- Materialized View --- p.22Chapter 2.2.1 --- What Is A Materialized View --- p.23Chapter 2.2.2 --- The Role of Materialized View in OLAP --- p.23Chapter 2.2.3 --- The Challenges in Exploiting Materialized View --- p.24Chapter 2.2.4 --- What Is View Maintenance --- p.25Chapter 2.3 --- View Selection --- p.27Chapter 2.3.1 --- Selection Strategy --- p.27Chapter 2.4 --- Summary --- p.32Chapter 3 --- Problem Definition --- p.33Chapter 3.1 --- View Selection Under Constraint --- p.33Chapter 3.2 --- The Lattice Framework for Maintenance Cost View Selection Prob- lem --- p.35Chapter 3.3 --- The Difficulties of Maintenance Cost View Selection Problem --- p.39Chapter 3.4 --- Summary --- p.41Chapter 4 --- What Difference Heuristics Make --- p.43Chapter 4.1 --- Motivation --- p.44Chapter 4.2 --- Example --- p.46Chapter 4.3 --- Existing Algorithms --- p.49Chapter 4.3.1 --- A*-Heuristic --- p.51Chapter 4.3.2 --- Inverted-Tree Greedy --- p.52Chapter 4.3.3 --- Two-Phase Greedy --- p.54Chapter 4.3.4 --- Integrated Greedy --- p.57Chapter 4.4 --- A Performance Study --- p.60Chapter 4.5 --- Summary --- p.68Chapter 5 --- Materialized View Selection as Constrained Evolutionary Opti- mization --- p.71Chapter 5.1 --- Motivation --- p.72Chapter 5.2 --- Evolutionary Algorithms --- p.73Chapter 5.2.1 --- Constraint Handling: Penalty v.s. Stochastic Ranking --- p.74Chapter 5.2.2 --- The New Stochastic Ranking Evolutionary Algorithm --- p.78Chapter 5.3 --- Experimental Studies --- p.81Chapter 5.3.1 --- Experimental Setup --- p.82Chapter 5.3.2 --- Experimental Results --- p.82Chapter 5.4 --- Summary --- p.89Chapter 6 --- Dynamic Materialized View Management Based On Predicates --- p.90Chapter 6.1 --- Motivation --- p.91Chapter 6.2 --- Examples --- p.93Chapter 6.3 --- Related Work: Static Prepartitioning-Based Materialized View Management --- p.96Chapter 6.4 --- A New Dynamic Predicate-based Partitioning Approach --- p.99Chapter 6.4.1 --- System Overview --- p.102Chapter 6.4.2 --- Partition Advisor --- p.103Chapter 6.4.3 --- View Manager --- p.104Chapter 6.5 --- A Performance Study --- p.108Chapter 6.5.1 --- Performance Metrics --- p.110Chapter 6.5.2 --- Feasibility Studies --- p.110Chapter 6.5.3 --- Query Locality --- p.112Chapter 6.5.4 --- The Effectiveness of Disk Size --- p.115Chapter 6.5.5 --- Scalability --- p.115Chapter 6.6 --- Summary --- p.116Chapter 7 --- Conclusions and Future Work --- p.118Bibliography --- p.12