|
| Titre : |
Data Warehousing, Data Mining And Olap |
| Type de document : |
texte imprimé |
| Auteurs : |
Alex Berson |
| Editeur : |
McGraw Hill |
| Année de publication : |
2017 |
| Importance : |
1 vol. (612 p.) |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-0-07-006272-6 |
| Langues : |
Français (fre) |
| Catégories : |
Informatique
|
| Mots-clés : |
Data Warehousing
Data Mining
Olap |
| Index. décimale : |
004 Informatique |
| Résumé : |
Optimize your organization's data delivery system! Improving data delivery is a top priority in business computing today. This comprehensive,cutting-edge guide can help—by showing you how to effectively integrate data mining and other powerful data warehousing technologies. You'll learn how to: Use data warehousing to establish a competitive advantage; Solve business problems faster by exploiting online analytical processing (OLAP); Evaluate various data warehousing solutions (including SMP and MPP,parallel database management systems,metadata,OLAP,etc. ); Leverage your data warehousing utility via the Internet,client/server computing,and various data mining tools. In addition to providing a detailed overview and strategic analysis of the available data warehousing technologies,the book serves as a practical guide to data warehouse database design,star and snowflake schema approaches,multidimensional and mutirelational models,advanced indexing techniques,and data mining. You'll also learn how to compare different data mine technologies and products,and understand how they fit into your overall business and data processes. Intended for IS professionals as well as strategic planners,this fascinating book can be well relied upon as the essential reference to the standards,tools,technologies—and possibilities—of data warehousing today |
| Note de contenu : |
Sommaire
PART I: FOUNDATION
Chapter 1 Introduction to Data Warehousing
Chapter 2 Client/Server Computing Model and Data Warehousing
Chapter 3 Parallel Processors and Cluster Systems
Chapter 4 Distributed DBMS Implementations
Chapter 5 Client/Server RDBMS Solutions
PART II: DATA WAREHOUSING
Chapter 6 Data Warehousing Components
Chapter 7 Building a Data Warehouse
Chapter 8 Mapping the Data Warehouse to a Multiprocessor Architecture
Chapter 9 DBMS Schemas for Decision Support
Chapter 10 Data Extraction, Cleanup, and Transformation Tools
Chapter 11 Metadata
PART III: BUSINESS ANALYSIS
Chapter 12 Reporting and Query Tools and Applications
Chapter 13 On-Line Analytical Processing (OLAP)
Chapter 14 Patterns and Models
Chapter 15 Statistics
Chapter 16 Artificial Intelligence
PART IV: DATA MINING
Chapter 17 Introduction to Data Mining
Chapter 18 Decision Trees
Chapter 19 Neural Networks
Chapter 20 Nearest Neighbor and Clustering
Chapter 21 Genetic Algorithms
Chapter 22 Rule Induction
Chapter 23 Selecting and Using the Right Technique
PART V: DATA VISUALIZATION AND OVERALL PERSPECTIVE
Chapter 24 Data Visualization
Chapter 25 Putting It All Together |
| Côte titre : |
Fs/19732 |
Data Warehousing, Data Mining And Olap [texte imprimé] / Alex Berson . - [S.l.] : McGraw Hill, 2017 . - 1 vol. (612 p.) ; 24 cm. ISBN : 978-0-07-006272-6 Langues : Français ( fre)
| Catégories : |
Informatique
|
| Mots-clés : |
Data Warehousing
Data Mining
Olap |
| Index. décimale : |
004 Informatique |
| Résumé : |
Optimize your organization's data delivery system! Improving data delivery is a top priority in business computing today. This comprehensive,cutting-edge guide can help—by showing you how to effectively integrate data mining and other powerful data warehousing technologies. You'll learn how to: Use data warehousing to establish a competitive advantage; Solve business problems faster by exploiting online analytical processing (OLAP); Evaluate various data warehousing solutions (including SMP and MPP,parallel database management systems,metadata,OLAP,etc. ); Leverage your data warehousing utility via the Internet,client/server computing,and various data mining tools. In addition to providing a detailed overview and strategic analysis of the available data warehousing technologies,the book serves as a practical guide to data warehouse database design,star and snowflake schema approaches,multidimensional and mutirelational models,advanced indexing techniques,and data mining. You'll also learn how to compare different data mine technologies and products,and understand how they fit into your overall business and data processes. Intended for IS professionals as well as strategic planners,this fascinating book can be well relied upon as the essential reference to the standards,tools,technologies—and possibilities—of data warehousing today |
| Note de contenu : |
Sommaire
PART I: FOUNDATION
Chapter 1 Introduction to Data Warehousing
Chapter 2 Client/Server Computing Model and Data Warehousing
Chapter 3 Parallel Processors and Cluster Systems
Chapter 4 Distributed DBMS Implementations
Chapter 5 Client/Server RDBMS Solutions
PART II: DATA WAREHOUSING
Chapter 6 Data Warehousing Components
Chapter 7 Building a Data Warehouse
Chapter 8 Mapping the Data Warehouse to a Multiprocessor Architecture
Chapter 9 DBMS Schemas for Decision Support
Chapter 10 Data Extraction, Cleanup, and Transformation Tools
Chapter 11 Metadata
PART III: BUSINESS ANALYSIS
Chapter 12 Reporting and Query Tools and Applications
Chapter 13 On-Line Analytical Processing (OLAP)
Chapter 14 Patterns and Models
Chapter 15 Statistics
Chapter 16 Artificial Intelligence
PART IV: DATA MINING
Chapter 17 Introduction to Data Mining
Chapter 18 Decision Trees
Chapter 19 Neural Networks
Chapter 20 Nearest Neighbor and Clustering
Chapter 21 Genetic Algorithms
Chapter 22 Rule Induction
Chapter 23 Selecting and Using the Right Technique
PART V: DATA VISUALIZATION AND OVERALL PERSPECTIVE
Chapter 24 Data Visualization
Chapter 25 Putting It All Together |
| Côte titre : |
Fs/19732 |
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