Data Completeness Overview

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Table of contents

1 Introduction and Motivation 
1.1 General Context and Motivation
1.2 EBITA and Smart Campus
1.3 Challenges by Example
1.3.1 Challenge #1: Complete and Missing Data Representation
1.3.2 Challenge #2: Query Result Annotation
1.3.3 Challenge #3: Aggregate Queries Correctness
1.3.4 Challenge #4: Aggregate Query Imputation
1.3.5 Challenge #5: Data Fragments Summarization
1.4 Thesis Contributions
1.5 Thesis Outline
I Relative Completeness Representation
2 Data Completeness Representation 
2.1 Introduction
2.2 Data Quality
2.2.1 Taxonomies of Data Quality Problems
2.2.2 Data Quality Dimensions
2.3 Data Completeness Overview
2.4 Data Completeness Representation Models
2.4.1 Missing Values Representation
2.4.2 Missing Tuples Representation
2.5 Summary
3 Pattern Model and Algebra 
3.1 Introduction
3.2 Relative Information Model
3.2.1 Constrained Tables
3.2.2 Assessing Data Completeness
3.3 The Pattern Model
3.3.1 Partition Patterns
3.3.2 Pattern Semantics
3.3.3 Pattern Covers
3.4 The Pattern Algebra
3.4.1 Pattern Operators
3.4.2 Rewriting Rules and Optimization
3.4.3 Safe Projection
3.5 Pattern Queries
3.6 Independent References
3.7 Summary
4 Pattern Algebra Implementation and Experiments 
4.1 Introduction
4.2 Translating Pattern Algebra Expression into SQL
4.3 Folding Data
4.4 Folding Patterns
4.5 Experiments
4.5.1 Datasets
4.5.2 Pattern table generation
4.5.3 Pattern Query Processing
4.5.4 Folding pattern query results
4.6 Summary
II Incomplete Query Result Imputation 
5 Data and query result imputation techniques 
5.1 Introduction
5.2 Handling the Missing Data Problem
5.3 Data Imputation
5.3.1 Human Based Imputation
5.3.2 Automatic Data Imputation
5.3.3 Summary
5.4 Query-driven Imputation
5.4.1 Approximate Query Processing
5.4.2 Dynamic Imputation
5.4.3 Missing Tuples Impact on Query Results
5.5 Summary
6 Query Result Imputation for Aggregation Queries 
6.1 Introduction
6.2 Motivation
6.3 Imputation Model
6.3.1 Aggregate Queries and Query Patterns
6.3.2 Imputation Rules and Imputation Queries
6.4 Query Imputation Process
6.4.1 Step 1: Annotating Query Results
6.4.2 Step 2: Generate Candidate Imputations
6.4.3 Step 3: Imputation Strategy
6.4.4 Step 4: Imputation Query Generation
6.5 Implementation
6.5.1 Partition Patterns Classification
6.5.2 Imputation Query SQL Implementation
6.6 Experiments
6.6.1 Query Result Annotation
6.6.2 Query Result Imputation
6.7 Summary
III Reasoning With Fragment Summaries 
7 Summarizing and comparing data fragments using patterns 
7.1 Introduction
7.2 Motivation
7.3 Fragment and Summary Model
7.3.1 Data Fragments
7.3.2 Fragment Summaries
7.4 Reasoning with Fragment Summaries
7.4.1 Formal Reasoning Model
7.4.2 Reasoning with Queries
7.5 Experiments
7.6 Related Work
7.7 Summary
IV Conclusion and Future Work 
8 Conclusion and perspectives 
8.1 General Conclusion
8.2 Perspectives on the Completeness Model
8.2.1 User-Friendly Interface
8.2.2 Incremental Minimal Covers
8.3 Perspectives on Query Result Imputation
8.3.1 Imputation Quality Model
8.3.2 Imputation Strategy
8.3.3 Shared Query Result Imputations