1
Topics about Data Warehouses
What is a data warehouse?
How does a data warehouse differ from a transaction processing database?
What are the characteristics of a data warehouse?
What are the components of a data warehousing system?
How is a data warehouse created?
How is a data warehouse accessed?
TPS vs. DSS
Issue TPS/MIS DSS
Definition Systems to support day-to-day operations.
Systems to support ad-hoc decision making.
Users clerks, data entry, low-level supervisors.
managers, analysts, support staff, researchers.
Design goal Performance. Flexibility, ease of use, ease of access.
Transaction Type
Updates. Queries.
Query Activity
low; few joins. high; many joins.
Transaction vs. DSS databases
Issue Transaction database
DSS database
Content Internal data, process-oriented.
Internal and external data.
Subject-oriented.
Data currency
Real time.
Current.
Volatile.
Batch.
Historical.
Non-volatile.
Summary level
Details of transactions; no (or very little) derived data.
Summarized; many aggregation levels.
Volume Megabytes to gigabytes.
Gigabytes to terabytes.
Design Normalized to prevent anomalies.
Denormalized to enhance query performance.
So, can one database support both transaction processing and decision
support applications?Yes No
What is a data warehouse?
A data warehouse is a database designed to support a decision support system.
A data warehouse is:
Integrated: It is a centralized, consolidated database integrating data from an entire organization.
Subject-oriented: Data warehouse data are organized around key subjects. The data are usually arranged by topic, such as customers, products, suppliers, etc.
Time-variant: Data in the warehouse contain a time dimension so that they may be used as a historical aggregation.
Non-volatile: Once data enter, they seldom leave. Data are appended rather than overwritten. Data are updated in batches.
Data warehouse design example
SalesFact
PK DayPK MonthPK YearPK,FK4 ProductIDPK,FK1 CustomerTypeIDPK,FK2 EmployeeIDPK,FK3 LocationID
SalesDollars #ofSales
Product
PK ProductID
Description
Employee
PK EmployeeID
Name
CustomerType
PK CustomerTypeID
Description
Location
PK LocationID
Description
7
Issues in designing a data warehouse
Must have a predefined subject focus.
Has the potential to be very large – must define the “grain” or granularity level of storage.
Will always have a dimension of time.
Will contain derived data.
Will be a summary of data, rather than each detailed transaction.
Does not always adhere to standard normalization rules.
CustomerTransactionDatabase
ProductTransactionDatabase
OrderTransactionDatabase
DataScrubbing
DataScrubbing
DataScrubbing
DataExtraction
DataExtraction
DataExtraction
DataIntegration
Sales DataWarehouse
Creating aData
Warehouse
9
Issues in creating a data warehouse
How to get accurate and complete data?
How to consolidate data?
Differing data meanings.
Differing storage mechanisms.
Differing data formats.
10
Components of a data warehousing system
Data store.
Extraction/filtering/transformation processes.
End user query tools.
End user visualization tools.
Two-tier data warehouse architecture
Data warehouse
Operationaldatabase
Operationaldatabase
Externaldata source
EDM
Summarizeddata
Transformationprocess
Data warehouseserver
User departments
Three-tier data warehouse architecture
Data warehouse
Operationaldatabase
Operationaldatabase
Externaldata source
EDM
Summarizeddata
Transformationprocess
Data warehouseserver
Userdepartments
Data mart
Data mart
Data mart tier
Extractionprocess
13
Accessing a data warehouse
Visualization tools.
Graphical.
Spreadsheet format - usually Excel or Lotus look-and-feel.
Dashboard. Example: http://tomcat.corda.com/superstore/sr.jsp
Query tools.
OLAP: Online analytical processing.
Data mining: Artificial intelligence based query methods.
14
Online analytical processing
Provides multi-dimensional data analysis techniques.
Works primarily with data aggregation.
Provides advanced statistical analysis.
Provides advanced graphical output.
Supports access to very large databases.
Provides enhanced query optimization algorithms.
Lots of acronyms: OLAP, ROLAP, MOLAP, HOLAP.
Can be add-ons to existing products, example is Excel. Can have their own user interfaces.
OLAP vs. Data Mining questionsOLAP Data Mining
Which customers spent the most with us in the past year?
Which types of customers are likely to spend the most with us in the coming year?
How much did the bank lose from loan defaulters within the past two years?
What are the characteristics of the customers most likely to default on their loans before the year is over?
What were the highest selling fashion items in our London stores?
What additional products are most likely to be sold to customers who buy shorts?
Which store/ location made the highest sales in the past year?
In which area whould we open a new store next year?
16
Data mining
Data mining tools:
analyze the data;
uncover patterns hidden in the data;
form computer models based on the findings; and
use the models to predict business behavior.
Proactive tools.
Based on artificial intelligence software such as decision trees, neural networks, fuzzy logic systems, inductive nets and classification networking.
17
What are some applications of data warehousing?
Customer relationship management.
Business process management.
Order management.
Strategic decision analysis.
