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Table of contents
1 Introduction
1.1 Introduction
1.1.1 Preliminaries
1.1.1.1 Data Model of XML Document
1.1.1.2 XPath
1.1.1.3 Recursion in XML Document
1.1.1.4 Document Depth
1.1.1.5 Stream-querying Process
1.1.1.6 Stream-filtering Process
1.1.1.7 Synopsis
1.1.1.8 Selectivity Estimation Technique
1.1.1.9 Performance Prediction Model
1.2 Challenges
1.2.1 The Expressiveness of XPath
1.2.2 Structure of XML Data Set
1.2.3 Query Evaluation Strategy
1.2.4 Evolution and Data Set Updating
1.3 Contributions
1.4 Thesis Organisation
1.4.1 The Dependency of Thesis’s Chapters
2 State of the Art
2.1 Introduction
2.2 Selectivity Estimation
2.2.1 Properties of Selectivity Estimation Techniques
2.2.2 Path/Twig Selectivity Estimation Techniques
2.2.2.1 Synopsis-Based Estimation Techniques
2.2.2.2 Histogram-Based Estimation Techniques
2.2.3 Summary – The Choice of the Path tree Synopsis
2.3 Stream-processing Approaches
2.3.1 Stream-filtering Algorithms
2.3.2 Stream-querying Algorithms
2.3.3 Summary – Lazy Stream-querying Algorithm LQ
3 Path tree: Definition, Construction, and Updating
3.1 Introduction
3.1.1 The XML Data Model
3.2 Path tree Definition
3.3 Path tree Construction: Automata Technique
3.3.1 Automaton Definition A
3.3.2 Automata Transformation into a Graph Doc(A)
3.3.3 Automata Minimization AMin
3.3.4 Example of Path tree Construction: Automata Technique 54
3.4 Path tree Construction: Streaming Technique
3.4.1 Path tree Construction
3.4.2 Path tree Updating
4 Selectivity Estimation Techniques
4.1 Introduction
4.2 Lazy Stream-querying Algorithm
4.2.1 Query Preprocessing
4.2.2 LQ – Blocks Extension
4.2.3 Examples of Query Processing Using LQ-Extended
4.2.3.1 Query Processing – Simple Path
4.2.3.2 Query Processing – Twig Path
4.3 Selectivity Estimation Algorithm
4.3.1 Examples of the Selectivity Estimation Process
4.3.1.1 Selectivity Estimation – Simple Path
4.3.1.2 Selectivity Estimation – Twig Path
4.3.2 Accuracy of the Selectivity Estimation Technique
5 Performance Prediction Model
5.1 Introduction
5.2 Performance Prediction Model- Preliminaries
5.2.1 Performance Prediction Model – Motivations
5.2.2 Performance Measurements Towards the Optimization of Stream-processing for XML Data
5.2.2.1 Prototype O-Search
5.2.2.2 Experimental Results
5.2.2.3 Conclusion
5.2.3 Performance Prediction Model – General Structure
5.3 Performance Prediction Model – Simple Path
5.3.1 Lazy Stream-querying Algorithm (LQ)
5.3.2 Building the Mathematical Model
5.3.3 Building the Prediction Model
5.3.3.1 Prediction Rules
5.3.4 User Protocol
5.3.5 Experimental Results
5.3.5.1 Experimental Setup
5.3.5.2 Quality of Model Prediction
5.3.5.3 Impact of Using Metadata in our Model on the Performance
5.3.5.4 Model Portability on Other Machines
5.3.6 Conclusion
5.4 Performance Prediction Model – Twig Path
5.4.1 Lazy Stream-querying Algorithm (LQ)
5.4.2 Building the Mathematical Model
5.4.3 Building the Prediction Model
5.4.3.1 Example of the Selectivity Estimation Process
5.4.4 Experimental Results
5.4.4.1 Experimental Setup
5.4.4.2 Accuracy of the Selectivity Estimation
5.4.4.3 Efficiency of the Selectivity Estimation Algorithm
5.4.4.4 Comparing our Approach with the other Approaches
5.4.5 Use Case: Online Stream-querying System
5.4.5.1 Online Stream-querying System
5.4.6 Conclusion and Future Work
6 Conclusion and Perspectives
6.1 Conclusion
6.2 Future Work
6.2.1 Stream-processing
6.2.2 Selectivity Estimation Technique
6.2.3 Parallel Processing
