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Data Warehouse Systems: Design and Implementation Softcover reprint of the original 1st ed. 2014 [Pehme köide]

  • Formaat: Paperback / softback, 625 pages, kõrgus x laius: 235x155 mm, kaal: 9708 g, 133 Illustrations, black and white; XVI, 625 p. 133 illus., 1 Paperback / softback
  • Sari: Data-Centric Systems and Applications
  • Ilmumisaeg: 23-Aug-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662513501
  • ISBN-13: 9783662513507
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  • Formaat: Paperback / softback, 625 pages, kõrgus x laius: 235x155 mm, kaal: 9708 g, 133 Illustrations, black and white; XVI, 625 p. 133 illus., 1 Paperback / softback
  • Sari: Data-Centric Systems and Applications
  • Ilmumisaeg: 23-Aug-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662513501
  • ISBN-13: 9783662513507
Teised raamatud teemal:
With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes Fundamental Concepts including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details Implementation and Deployment, which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers Advanced Topics such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases.

As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter.

Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.

Arvustused

The book is very well suited for one or more data warehouse courses, ranging from the most basic to the most advanced. It has all the features that are necessary to make a good textbook. () The one thing which really set this book apart from its peers is the coverage of advanced data warehouse topics () The book also provides a useful overview of novel Big Data" technologies like Hadoop, and novel database and data warehouse architectures like in-memory databases, column stores, and right-time data warehouses. from the foreword by Torben Bach Pedersen, Aalborg Universiteit, Denmark

Part I Fundamental Concepts
1 Introduction
3(10)
1.1 A Historical Overview of Data Warehousing
4(4)
1.2 Spatial and Spatiotemporal Data Warehouses
8(1)
1.3 New Domains and Challenges
9(2)
1.4 Review Questions
11(2)
2 Database Concepts
13(40)
2.1 Database Design
13(2)
2.2 The Northwind Case Study
15(1)
2.3 Conceptual Database Design
16(5)
2.4 Logical Database Design
21(22)
2.4.1 The Relational Model
21(6)
2.4.2 Normalization
27(3)
2.4.3 Relational Query Languages
30(13)
2.5 Physical Database Design
43(3)
2.6 Summary
46(1)
2.7 Bibliographic Notes
47(1)
2.8 Review Questions
47(1)
2.9 Exercises
48(5)
3 Data Warehouse Concepts
53(36)
3.1 Multidimensional Model
53(6)
3.1.1 Hierarchies
56(1)
3.1.2 Measures
57(2)
3.2 OLAP Operations
59(13)
3.3 Data Warehouses
72(4)
3.4 Data Warehouse Architecture
76(4)
3.4.1 Back-End Tier
76(1)
3.4.2 Data Warehouse Tier
77(1)
3.4.3 OLAP Tier
78(1)
3.4.4 Front-End Tier
79(1)
3.4.5 Variations of the Architecture
79(1)
3.5 Data Warehouse Design
80(1)
3.6 Business Intelligence Tools
81(3)
3.6.1 Overview of Microsoft SQL Server Tools
82(1)
3.6.2 Overview of Pentaho Business Analytics
83(1)
3.7 Summary
84(1)
3.8 Bibliographic Notes
84(1)
3.9 Review Questions
85(1)
3.10 Exercises
86(3)
4 Conceptual Data Warehouse Design
89(32)
4.1 Conceptual Modeling of Data Warehouses
89(5)
4.2 Hierarchies
94(12)
4.2.1 Balanced Hierarchies
95(1)
4.2.2 Unbalanced Hierarchies
95(1)
4.2.3 Generalized Hierarchies
96(2)
4.2.4 Alternative Hierarchies
98(1)
4.2.5 Parallel Hierarchies
99(3)
4.2.6 Nonstrict Hierarchies
102(4)
4.3 Advanced Modeling Aspects
106(4)
4.3.1 Facts with Multiple Granularities
106(1)
4.3.2 Many-to-Many Dimensions
106(4)
4.4 Querying the Northwind Cube Using the OLAP Operations
110(4)
4.5 Summary
114(1)
4.6 Bibliographic Notes
115(1)
4.7 Review Questions
116(1)
4.8 Exercises
116(5)
5 Logical Data Warehouse Design
121(58)
5.1 Logical Modeling of Data Warehouses
121(2)
5.2 Relational Data Warehouse Design
123(3)
5.3 Relational Implementation of the Conceptual Model
126(2)
5.4 Time Dimension
128(1)
5.5 Logical Representation of Hierarchies
129(7)
5.5.1 Balanced Hierarchies
129(1)
5.5.2 Unbalanced Hierarchies
130(2)
5.5.3 Generalized Hierarchies
132(2)
5.5.4 Alternative Hierarchies
134(1)
5.5.5 Parallel Hierarchies
134(1)
5.5.6 Nonstrict Hierarchies
135(1)
5.6 Advanced Modeling Aspects
136(3)
5.6.1 Facts with Multiple Granularities
137(1)
5.6.2 Many-to-Many Dimensions
138(1)
5.7 Slowly Changing Dimensions
139(6)
5.8 SQL/OLAP Operations
145(7)
5.8.1 Data Cube
146(1)
5.8.2 ROLLUP, CUBE, and GROUPING SETS
147(2)
5.8.3 Window Functions
149(3)
5.9 Definition of the Northwind Cube in Analysis Services
152(12)
5.9.1 Data Sources
152(1)
5.9.2 Data Source Views
152(2)
5.9.3 Dimensions
154(4)
5.9.4 Hierarchies
158(3)
5.9.5 Cubes
161(3)
5.10 Definition of the Northwind Cube in Mondrian
164(9)
5.10.1 Schemas and Physical Schemas
165(1)
5.10.2 Cubes, Dimensions, Attributes, and Hierarchies
166(5)
5.10.3 Measures
171(2)
5.11 Summary
173(1)
5.12 Bibliographic Notes
173(1)
5.13 Review Questions
173(1)
5.14 Exercises
174(5)
6 Querying Data Warehouses
179(54)
6.1 Introduction to MDX
180(27)
6.1.1 Tuples and Sets
180(1)
6.1.2 Basic Queries
181(2)
6.1.3 Slicing
183(2)
6.1.4 Navigation
185(3)
6.1.5 Cross Join
188(1)
6.1.6 Subqueries
189(2)
6.1.7 Calculated Members and Named Sets
191(2)
6.1.8 Relative Navigation
193(3)
6.1.9 Time Series Functions
196(4)
6.1.10 Filtering
200(1)
6.1.11 Sorting
201(2)
6.1.12 Top and Bottom Analysis
203(2)
6.1.13 Aggregation Functions
205(2)
6.2 Querying the Northwind Cube in MDX
207(9)
6.3 Querying the Northwind Data Warehouse in SQL
216(9)
6.4 Comparison of MDX and SQL
225(2)
6.5 Summary
227(1)
6.6 Bibliographic Notes
228(2)
6.7 Review Questions
230(1)
6.8 Exercises
230(3)
Part II Implementation and Deployment
7 Physical Data Warehouse Design
233(52)
7.1 Physical Modeling of Data Warehouses
234(1)
7.2 Materialized Views
235(5)
7.2.1 Algorithms Using Full Information
237(2)
7.2.2 Algorithms Using Partial Information
239(1)
7.3 Data Cube Maintenance
240(6)
7.4 Computation of a Data Cube
246(10)
7.4.1 PipeSort Algorithm
247(3)
7.4.2 Cube Size Estimation
250(1)
7.4.3 Partial Computation of a Data Cube
251(5)
7.5 Indexes for Data Warehouses
256(5)
7.5.1 Bitmap Indexes
257(2)
7.5.2 Bitmap Compression
259(1)
7.5.3 Join Indexes
260(1)
7.6 Evaluation of Star Queries
261(2)
7.7 Data Warehouse Partitioning
263(3)
7.7.1 Queries in Partitioned Databases
264(1)
7.7.2 Managing Partitioned Databases
265(1)
7.7.3 Partitioning Strategies
265(1)
7.8 Physical Design in SQL Server and Analysis Services
266(8)
7.8.1 Indexed Views
266(1)
7.8.2 Partition-Aligned Indexed Views
267(2)
7.8.3 Column-Store Indexes
269(1)
7.8.4 Partitions in Analysis Services
269(5)
7.9 Query Performance in Analysis Services
274(2)
7.10 Query Performance in Mondrian
276(2)
7.10.1 Aggregate Tables
276(1)
7.10.2 Caching
277(1)
7.11 Summary
278(1)
7.12 Bibliographic Notes
279(1)
7.13 Review Questions
279(1)
7.14 Exercises
280(5)
8 Extraction, Transformation, and Loading
285(44)
8.1 Business Process Modeling Notation
286(5)
8.2 Conceptual ETL Design Using BPMN
291(4)
8.3 Conceptual Design of the Northwind ETL Process
295(14)
8.4 Integration Services and Kettle
309(3)
8.4.1 Overview of Integration Services
309(2)
8.4.2 Overview of Kettle
311(1)
8.5 The Northwind ETL Process in Integration Services
312(7)
8.6 The Northwind ETL Process in Kettle
319(5)
8.7 Summary
324(1)
8.8 Bibliographic Notes
325(1)
8.9 Review Questions
325(1)
8.10 Exercises
326(3)
9 Data Analytics: Exploiting the Data Warehouse
329(56)
9.1 Data Mining
330(32)
9.1.1 Data Mining Tasks
331(2)
9.1.2 Supervised Classification
333(3)
9.1.3 Clustering
336(2)
9.1.4 Association Rules
338(6)
9.1.5 Pattern Growth Algorithm
344(3)
9.1.6 Sequential Patterns
347(3)
9.1.7 Data Mining in Analysis Services
350(12)
9.2 Key Performance Indicators
362(8)
9.2.1 Classification of Key Performance Indicators
363(1)
9.2.2 Guidelines for Defining Key Performance Indicators
364(2)
9.2.3 KPIs for the Northwind Case Study
366(1)
9.2.4 KPIs in Analysis Services
367(3)
9.3 Dashboards
370(8)
9.3.1 Types of Dashboards
371(1)
9.3.2 Guidelines for Dashboard Design
372(1)
9.3.3 Dashboards in Reporting Services
373(5)
9.4 Summary
378(1)
9.5 Bibliographic Notes
378(1)
9.6 Review Questions
379(1)
9.7 Exercises
380(5)
10 A Method for Data Warehouse Design
385(42)
10.1 Approaches to Data Warehouse Design
386(2)
10.2 General Overview of the Method
388(1)
10.3 Requirements Specification
389(13)
10.3.1 Analysis-Driven Requirements Specification
389(3)
10.3.2 Analysis-Driven Requirements for the Northwind Case Study
392(4)
10.3.3 Source-Driven Requirements Specification
396(2)
10.3.4 Source-Driven Requirements for the Northwind Case Study
398(3)
10.3.5 Analysis/Source-Driven Requirements Specification
401(1)
10.4 Conceptual Design
402(8)
10.4.1 Analysis-Driven Conceptual Design
402(2)
10.4.2 Analysis-Driven Conceptual Design for the Northwind Case Study
404(3)
10.4.3 Source-Driven Conceptual Design
407(1)
10.4.4 Source-Driven Conceptual Design for the Northwind Case Study
408(1)
10.4.5 Analysis/Source-Driven Conceptual Design
409(1)
10.5 Logical Design
410(3)
10.5.1 Logical Schemas
411(2)
10.5.2 ETL Processes
413(1)
10.6 Physical Design
413(2)
10.7 Characterization of the Various Approaches
415(3)
10.7.1 Analysis-Driven Approach
415(1)
10.7.2 Source-Driven Approach
416(1)
10.7.3 Analysis/Source-Driven Approach
417(1)
10.8 Summary
418(1)
10.9 Bibliographic Notes
418(1)
10.10 Review Questions
419(1)
10.11 Exercises
420(7)
Part III Advanced Topics
11 Spatial Data Warehouses
427(48)
11.1 General Concepts of Spatial Databases
428(6)
11.1.1 Spatial Data Types
428(4)
11.1.2 Continuous Fields
432(2)
11.2 Conceptual Modeling of Spatial Data Warehouses
434(8)
11.2.1 Spatial Hierarchies
438(2)
11.2.2 Spatiality and Measures
440(2)
11.3 Implementation Considerations for Spatial Data
442(6)
11.3.1 Spatial Reference Systems
442(1)
11.3.2 Vector Model
443(3)
11.3.3 Raster Model
446(2)
11.4 Relational Representation of Spatial Data Warehouses
448(6)
11.4.1 Spatial Levels and Attributes
448(2)
11.4.2 Spatial Facts, Measures, and Hierarchies
450(2)
11.4.3 Topological Constraints
452(2)
11.5 GeoMondrian
454(1)
11.6 Querying the GeoNorthwind Cube in MDX
455(4)
11.7 Querying the GeoNorthwind Data Warehouse in SQL
459(2)
11.8 Spatial Data Warehouse Design
461(6)
11.8.1 Requirements Specification and Conceptual Design
462(5)
11.8.2 Logical and Physical Design
467(1)
11.9 Summary
467(1)
11.10 Bibliographic Notes
468(1)
11.11 Review Questions
468(1)
11.12 Exercises
469(6)
12 Trajectory Data Warehouses
475(32)
12.1 Mobility Data Analysis
476(1)
12.2 Temporal Types
477(8)
12.2.1 Temporal Spatial Types
481(2)
12.2.2 Temporal Field Types
483(2)
12.3 Implementation of Temporal Types in PostGIS
485(5)
12.4 The Northwind Trajectory Data Warehouse
490(5)
12.5 Querying the Northwind Trajectory Data Warehouse in SQL
495(7)
12.6 Summary
502(1)
12.7 Bibliographic Notes
502(1)
12.8 Review Questions
503(1)
12.9 Exercises
504(3)
13 New Data Warehouse Technologies
507(32)
13.1 MapReduce and Hadoop
508(2)
13.2 High-Level Languages for Hadoop
510(4)
13.2.1 Hive
510(2)
13.2.2 Pig Latin
512(2)
13.3 Column-Store Database Systems
514(2)
13.4 In-Memory Database Systems
516(3)
13.5 Representative Systems
519(9)
13.5.1 Vertica
519(1)
13.5.2 MonetDB
520(1)
13.5.3 MonetDB/X100
521(1)
13.5.4 SAP HANA
522(2)
13.5.5 Oracle TimesTen
524(2)
13.5.6 SQL Server xVelocity
526(2)
13.6 Real-Time Data Warehouses
528(4)
13.7 Extraction, Loading, and Transformation
532(2)
13.8 Summary
534(1)
13.9 Bibliographic Notes
535(1)
13.10 Review Questions
535(1)
13.11 Exercises
536(3)
14 Data Warehouses and the Semantic Web
539(38)
14.1 Semantic Web
540(7)
14.1.1 Introduction to RDF and RDFS
540(1)
14.1.2 RDF Serializations
541(2)
14.1.3 RDF Representation of Relational Data
543(4)
14.2 SPARQL
547(4)
14.3 RDF Representation of Multidimensional Data
551(10)
14.3.1 RDF Data Cube Vocabulary
553(4)
14.3.2 QB4OLAP Vocabulary
557(4)
14.4 Representation of the Northwind Cube in QB4OLAP
561(3)
14.5 Querying the Northwind Cube in SPARQL
564(9)
14.6 Summary
573(1)
14.7 Bibliographic Notes
574(1)
14.8 Review Questions
575(1)
14.9 Exercises
575(2)
15 Conclusion
577(12)
15.1 Temporal Data Warehouses
577(2)
15.2 3D/4D Spatial Data Warehouses
579(2)
15.3 Text Analytics and Text Data Warehouses
581(2)
15.4 Multimedia Data Warehouses
583(3)
15.5 Graph Analytics and Graph Data Warehouses
586(3)
A Graphical Notation
589(12)
A.1 Entity-Relationship Model
589(2)
A.2 Relational Model
591(1)
A.3 MultiDim Model for Data Warehouses
591(4)
A.4 MultiDim Model for Spatial Data Warehouses
595(2)
A.5 BPMN Notation for ETL
597(4)
References 601(14)
Index 615
Alejandro A. Vaisman is a professor at the Buenos Aires Institute of Technology (ITBA). He was previously professor at the University of Buenos Aires where he co-founded and chaired the Masters program in Data Mining. His research interests are in the field of databases, particularly in data warehousing and OLAP, business intelligence, spatiotemporal databases and the semantic web.

Esteban Zimányi is a professor and Director of the Department of Computer and Decision Engineering at the Université Libre de Bruxelles. His current research interests include data warehouses and business intelligence, geographical information systems and spatio-temporal databases. He is Editor-in-Chief of the Journal on Data Semantics. He is coordinator of the Erasmus Mundus Master and Doctorate on Information Technologies for Business Intelligence (IT4BI).