数据仓库和OLAP技术讲座培训(PPT 66页)

数据仓库和OLAP技术
数据库的定义
两种不同的数据处理需求
为什么要建立数据仓库？
What is Data Warehouse?
Data Warehouse—Subject-Oriented
面向应用举例
面向主题举例
Data Warehouse—Integrated
Data Warehouse—Time Variant
Data Warehouse—Non-Volatile
Data Warehouse vs. Heterogeneous DBMS
Data Warehouse vs. Operational DBMS
OLTP vs. OLAP
Why Separate Data Warehouse?
Data Warehousing and OLAPTechnology
From Tables and Spreadsheets to Data Cubes
Cube: A Lattice of Cuboids
Conceptual Modeling of Data Warehouses
Example of Star Schema
Example of Snowflake Schema
Example of Fact Constellation
A Data Mining Query Language,DMQL: Language Primitives
Defining a Star Schema in DMQL
Defining a Snowflake Schema in DMQL
Defining a Fact Constellation in DMQL
Measures: Three Categories
A Concept Hierarchy: Dimension (location)
View of Warehouses and Hierarchies
Multidimensional Data
A Sample Data Cube
Cuboids Corresponding to the Cube
Browsing a Data Cube
Typical OLAP Operations
A Star-Net Query Model
Data Warehousing and OLAP Technology for Data Mining
Design of a Data Warehouse: A Business Analysis Framework
Data Warehouse Design Process
Multi-Tiered Architecture
体系结构 [ Pieter ,1998 ]
数据仓库的焦点问题
ETL 工具
Three Data Warehouse Models
Data Warehouse Development:A Recommended Approach
OLAP Server Architectures
Data Warehousing and OLAP Technology for Data Mining
Efficient Data Cube Computation
Cube Operation
Cube Computation: ROLAP-Based Method
Multi-way Array Aggregation for Cube Computation
Indexing OLAP Data: Bitmap Index
Indexing OLAP Data: Join Indices
Efficient Processing OLAP Queries
Metadata Repository
Data Warehouse Back-End Tools and Utilities
Examples: Discovery-Driven Data Cubes
Data Warehouse Usage
An OLAM Architecture
Summary
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