Data Warehouse, Data Warehouse Architecture, Data Warehouse Concept, Data Warehouse Modeling, OLAP, OLAP Operations, Data Cube, Data Processing, Data Cleaning, Data Reduction, Data Integration, Data Transformation

1. DATA WAREHOUSE
by
Sonali Chawla

2. INTRODUCTION TO DATA WAREHOUSE
 Subject-oriented
 Integrated
 Time Variant
 Non Volatile

3. DATA WAREHOUSE CONCEPTS
 Operational Data and Informational Data
 Difference between Operational Data and Data
Warehouse
 Why have a separate Data Warehouse?

6. DATA WAREHOUSE ARCHITECTURE
 Steps of the Design and Construction of Data
Warehouse
 The design of data warehouse
 The process of data warehouse design
 Three tier architecture
 Model
 Enterprise Warehouse
 Data Marts
 Virtual Warehouse

7.  Metadata Repository
 Data Warehouse Back-End Tools and Utilities
 Data Extraction
 Data Clearing
 Data Transformation
 Load
 Refresh
 Advantages of Building Data Warehouse
 Data Warehouse Application
 Information Processing
 Analytical Processing
 Data Mining

8. DATABASE DATA MODELING
 Star Schema
 Snowflake Schema
 Facts Constellation Schema

9. OLAP AND DATA CUBE
 OLAP
 Data Cube
 Measures
 Distributive
 Algebraic
 Holistic
 Concept Hierarchies

10.  Operations on Cubes
 Roll Up
 Drill Down
 Slice and Dice
 Pivot (rotate)
 Drill Across
 Drill Through
 OLAP Server
 Relational OLAP Server
 Multidimensional OLAP Server
 Hybrid OLAP Server

11. DATA PROCESSING
 Data Cleaning
 Look for Missing Values
 Ignore the tuples
 Fill in the missing values manually
 Use a global constant to fill in the missing values
 Use the attribute mean to fill the missing values
 Use the attribute mean for all samples belonging to the same
class as given tuple
 Use the most probable value to fill in the missing value

12.  Noisy Data
 Binning
 Smoothing by bin mean
 Smoothing by bin boundaries
 Regression
 Linear
 Multiple Linear
 Clustering

13.  Data Integration
 Entity Identification Problem
 Redundancy
 Detection and Resolution of Data value conflicts
 Data Transformation
 Smoothing
 Aggregation
 Generalization
 Normalization
 Attribute Construction

14.  Data Reduction
 Data cube aggregation
 Attribute Subset Selection
 Dimensionality Reduction
 Numerosity Reduction
 Discretization and concept hierarchy generation