دانلود کتاب Data Warehouse Systems: Design and Implementation (Data-Centric Systems and Applications)

فهرست مطالب :

Foreword to the Second Edition
Foreword to the First Edition
Preface
Objective of the Book
Organization of the Book and Teaching Paths
Acknowledgments
About the Authors
Contents
Part I Fundamental Concepts
Chapter 1 Introduction
1.1 An Overview of Data Warehousing
1.2 Emerging Data Warehousing Technologies
1.3 Review Questions
Chapter 2 Database Concepts
2.1 Database Design
2.2 The Northwind Case Study
2.3 Conceptual Database Design
2.4 Logical Database Design
2.4.1 The Relational Model
2.4.2 Normalization
2.4.3 Relational Query Languages
2.5 Physical Database Design
2.6 Summary
2.7 Bibliographic Notes
2.8 Review Questions
2.9 Exercises
Chapter 3 Data Warehouse Concepts
3.1 Multidimensional Model
3.1.1 Hierarchies
3.1.2 Measures
3.2 OLAP Operations
3.3 Data Warehouses
3.4 Data Warehouse Architecture
3.4.1 Back-End Tier
3.4.2 Data Warehouse Tier
3.4.3 OLAP Tier
3.4.4 Front-End Tier
3.4.5 Variations of the Architecture
3.5 Overview of Microsoft SQL Server BI Tools
3.6 Summary
3.7 Bibliographic Notes
3.8 Review Questions
3.9 Exercises
Chapter 4 Conceptual Data Warehouse Design
4.1 Conceptual Modeling of Data Warehouses
4.2 Hierarchies
4.2.1 Balanced Hierarchies
4.2.2 Unbalanced Hierarchies
4.2.3 Generalized Hierarchies
4.2.4 Alternative Hierarchies
4.2.5 Parallel Hierarchies
4.2.6 Nonstrict Hierarchies
4.3 Advanced Modeling Aspects
4.3.1 Facts with Multiple Granularities
4.3.2 Many-to-Many Dimensions
4.3.3 Links between Facts
4.4 Querying the Northwind Cube Using the OLAP Operations
4.5 Summary
4.6 Bibliographic Notes
4.7 Review Questions
4.8 Exercises
Chapter 5 Logical Data Warehouse Design
5.1 Logical Modeling of Data Warehouses
5.2 Relational Data Warehouse Design
5.3 Relational Representation of Data Warehouses
5.4 Time Dimension
5.5 Logical Representation of Hierarchies
5.5.1 Balanced Hierarchies
5.5.2 Unbalanced Hierarchies
5.5.3 Generalized Hierarchies
5.5.4 Alternative Hierarchies
5.5.5 Parallel Hierarchies
5.5.6 Nonstrict Hierarchies
5.6 Advanced Modeling Aspects
5.6.1 Facts with Multiple Granularities
5.6.2 Many-to-Many Dimensions
5.6.3 Links between Facts
5.7 Slowly Changing Dimensions
5.8 Performing OLAP Queries with SQL
5.9 Defining the Northwind Data Warehouse in Analysis Services
5.9.1 Multidimensional Model
5.9.2 Tabular Model
5.10 Summary
5.11 Bibliographic Notes
5.12 Review Questions
5.13 Exercises
Chapter 6 Data Analysis in Data Warehouses
6.1 Introduction to MDX
6.1.1 Tuples and Sets
6.1.2 Basic Queries
6.1.3 Slicing
6.1.4 Navigation
6.1.5 Cross Join
6.1.6 Subqueries
6.1.7 Calculated Members and Named Sets
6.1.8 Relative Navigation
6.1.9 Time-Related Calculations
6.1.10 Filtering
6.1.11 Sorting
6.1.12 Top and Bottom Analysis
6.1.13 Aggregation Functions
6.2 Introduction to DAX
6.2.1 Expressions
6.2.2 Evaluation Context
6.2.3 Queries
6.2.4 Filtering
6.2.5 Hierarchy Handling
6.2.6 Time-Related Calculations
6.2.7 Top and Bottom Analysis
6.2.8 Table Operations
6.3 Key Performance Indicators
6.3.1 Classification of Key Performance Indicators
6.3.2 Defining Key Performance Indicators
6.4 Dashboards
6.4.1 Types of Dashboards
6.4.2 Guidelines for Dashboard Design
6.5 Summary
6.6 Bibliographic Notes
6.7 Review Questions
Chapter 7 Data Analysis in the Northwind Data Warehouse
7.1 Querying the Multidimensional Model in MDX
7.2 Querying the Tabular Model in DAX
7.3 Querying the Relational Data Warehouse in SQL
7.4 Comparison of MDX, DAX, and SQL
7.5 KPIs for the Northwind Case Study
7.5.1 KPIs in Analysis Services Multidimensional
7.5.2 KPIs in Analysis Services Tabular
7.6 Dashboards for the Northwind Case Study
7.6.1 Dashboards in Reporting Services
7.6.2 Dashboards in Power BI
7.7 Summary
7.8 Review Questions
7.9 Exercises
Part II Implementation and Deployment
Chapter 8 Physical Data Warehouse Design
8.1 Physical Modeling of Data Warehouses
8.2 Materialized Views
8.2.1 Algorithms Using Full Information
8.2.2 Algorithms Using Partial Information
8.3 Data Cube Maintenance
8.4 Computation of a Data Cube
8.4.1 PipeSort Algorithm
8.4.2 Cube Size Estimation
8.4.3 Partial Computation of a Data Cube
8.5 Indexes for Data Warehouses
8.5.1 Bitmap Indexes
8.5.2 Bitmap Compression
8.5.3 Join Indexes
8.6 Evaluation of Star Queries
8.7 Partitioning
8.8 Parallel Processing
8.9 Physical Design in SQL Server and Analysis Services
8.9.1 Indexed Views
8.9.2 Partition-Aligned Indexed Views
8.9.3 Column-Store Indexes
8.9.4 Partitions in Analysis Services
8.10 Query Performance in Analysis Services
8.11 Summary
8.12 Bibliographic Notes
8.13 Review Questions
8.14 Exercises
Chapter 9 Extraction, Transformation, and Loading
9.1 Business Process Modeling Notation
9.2 Conceptual ETL Design Using BPMN
9.3 Conceptual Design of the Northwind ETL Process
9.4 SQL Server Integration Services
9.5 The Northwind ETL Process in Integration Services
9.6 Implementing ETL Processes in SQL
9.7 Summary
9.8 Bibliographic Notes
9.9 Review Questions
9.10 Exercises
Chapter 10 A Method for Data Warehouse Design
10.1 Approaches to Data Warehouse Design
10.2 General Overview of the Method
10.3 Requirements Specification
10.3.1 Business-Driven Requirements Specification
10.3.2 Data-driven Requirements Specification
10.3.3 Business/Data-driven Requirements Specification
10.4 Conceptual Design
10.4.1 Business-Driven Conceptual Design
10.4.2 Data-driven Conceptual Design
10.4.3 Business/Data-driven Conceptual Design
10.5 Logical Design
10.5.1 Logical Schemas
10.5.2 ETL Processes
10.6 Physical Design
10.7 Characterization of the Various Approaches
10.7.1 Business-Driven Approach
10.7.2 Data-driven Approach
10.7.3 Business/Data-driven Approach
10.8 Summary
10.9 Bibliographic Notes
10.10 Review Questions
10.11 Exercises
Part III Advanced Topics
Chapter 11 Temporal and Multiversion Data Warehouses
11.1 Manipulating Temporal Information in SQL
11.2 Conceptual Design of Temporal Data Warehouses
11.2.1 Time Data Types
11.2.2 Synchronization Relationships
11.2.3 A Conceptual Model for Temporal Data Warehouses
11.2.4 Temporal Hierarchies
11.2.5 Temporal Facts
11.3 Logical Design of Temporal Data Warehouses
11.4 Implementation Considerations
11.4.1 Period Encoding
11.4.2 Tables for Temporal Roll-Up
11.4.3 Integrity Constraints
11.4.4 Measure Aggregation
11.4.5 Temporal Measures
11.5 Querying the Temporal Northwind Data Warehouse in SQL
11.6 Temporal Data Warehouses versus Slowly Changing Dimensions
11.7 Conceptual Design of Multiversion Data Warehouses
11.8 Logical Design of Multiversion Data Warehouses
11.9 Querying the Multiversion Northwind Data Warehouse in SQL
11.10 Summary
11.11 Bibliographic Notes
11.12 Review Questions
11.13 Exercises
Chapter 12 Spatial and Mobility Data Warehouses
12.1 Conceptual Design of Spatial Data Warehouses
12.1.1 Spatial Data Types
12.1.2 Topological relationships
12.1.3 Continuous Fields
12.1.4 A Conceptual Model of Spatial Data Warehouses
12.2 Implementation Considerations for Spatial Data
12.2.1 Spatial Reference Systems
12.2.2 Vector Model
12.2.3 Raster Model
12.3 Logical Design of Spatial Data Warehouses
12.4 Topological Constraints
12.5 Querying the GeoNorthwind Data Warehouse in SQL
12.6 Mobility Data Analysis
12.7 Temporal Types
12.8 Temporal Types in MobilityDB
12.9 Mobility Data Warehouses
12.10 Querying the Northwind Mobility Data Warehouse in SQL
12.11 Summary
12.12 Bibliographic Notes
12.13 Review Questions
12.14 Exercises
Chapter 13 Graph Data Warehouses
13.1 Graph Data Models
13.2 Property Graph Database Systems
13.2.1 Neo4j
13.2.2 Introduction to Cypher
13.2.3 Querying the Northwind Cube with Cypher
13.3 OLAP on Hypergraphs
13.3.1 Operations on Hypergraphs
13.3.2 OLAP on Trajectory Graphs
13.4 Graph Processing Frameworks
13.4.1 Gremlin
13.4.2 JanusGraph
13.5 Bibliographic Notes
13.6 Review Questions
13.7 Exercises
Chapter 14 Semantic Web Data Warehouses
14.1 Semantic Web
14.1.1 Introduction to RDF and RDFS
14.1.2 RDF Serializations
14.1.3 RDF Representation of Relational Data
14.2 Introduction to SPARQL
14.2.1 SPARQL Basics
14.2.2 SPARQL Semantics
14.3 RDF Representation of Multidimensional Data
14.4 Representation of the Northwind Cube in QB4OLAP
14.5 Querying the Northwind Cube in SPARQL
14.6 Summary
14.7 Bibliographic Notes
14.8 Review Questions
14.9 Exercises
Chapter 15 Recent Developments in Big Data Warehouses
15.1 Data Warehousing in the Age of Big Data
15.2 Distributed Processing Frameworks
15.2.1 Hadoop
15.2.2 Hive
15.2.3 Spark
15.2.4 Comparison of Hadoop and Spark
15.2.5 Kylin
15.3 Distributed Database Systems
15.3.1 MySQL Cluster
15.3.2 Citus
15.4 In-Memory Database Systems
15.4.1 Oracle TimesTen
15.4.2 Redis
15.5 Column-Store Database Systems
15.5.1 Vertica
15.5.2 MonetDB
15.5.3 Citus Columnar
15.6 NoSQL Database Systems
15.6.1 HBase
15.6.2 Cassandra
15.7 NewSQL Database Systems
15.7.1 Cloud Spanner
15.7.2 SAP HANA
15.7.3 VoltDB
15.8 Array Database Systems
15.8.1 Rasdaman
15.8.2 SciDB
15.9 Hybrid Transactional and Analytical Processing
15.9.1 SingleStore
15.9.2 LeanXcale
15.10 Polystores
15.10.1 CloudMdsQL
15.10.2 BigDAWG
15.11 Cloud Data Warehouses
15.12 Data Lakes and Data Lakehouses
15.13 Future Perspectives
15.14 Summary
15.15 Bibliographic Notes
15.16 Review Questions
Appendix A Graphical Notation
A.1 Entity-Relationship Model
A.2 Relational Model
A.3 MultiDim Model for Data Warehouses
A.4 MultiDim Model for Spatial Data Warehouses
A.5 MultiDim Model for Temporal Data Warehouses
A.6 BPMN Notation for ETL
References
Glossary
Index