1. Overview of Data Warehousing (DW) and OLAP
Dinko Bačić

2. Agenda Timeline Definitions and Model DW Characteristics Data Modeling
Multidimensional models
Multidimensional schemas
Building DW
Issues/Current trends
OLAP demo

3. Business Data Warehouse
DW-Centric Timeline
Mainframe Era
Relational
PCs
OLAP
’50s
‘70
mid ‘70s
‘93
Business Data Warehouse
Top-down Design
CONCEPT
Bottoms-up Design
SOURCE
1988
1991
1996
NAME
Barry Devlin and Paul Murphy
Bill Inmon
Ralph Kimball

4. DW Definitions
A subject-oriented, integrated, non-volatile, time-variant, collection of data in support of management’s decisions (Inmon, 1993)
A copy of transaction data specifically structured for query and analysis (Kimball, 1996)
A collection of decision support technologies, aimed at enabling the knowledge worker to make better and faster decisions (Chaudhari and Dayal, 1997)

6. DW Characteristics
1. Multidimensional conceptual view 2. Transparency 3. Accessibility 4. Consistent reporting performance 5. Client/server architecture 6. Generic dimensionality

7. DW Characteristics
7. Dynamic sparse matrix handling 8. Multi-user support 9. Unrestricted cross-dimensional operations 10. Intuitive data manipulation 11. Flexible reporting 12. Unlimited dimensions and aggregation levels

8. Data Modeling

9. Data Modeling

10. Data Modeling

11. Data Modeling

12. Data Modeling – Star Schema

13. Data Modeling – Snowflake Schema

14. Data Modeling – Fact Constellation

15. Building DW Data must be: extracted from multiple sources
formatted for consistency
cleaned to ensure validity
fitted into the data model of DW
loaded into the DW

16. Issues Implementation issues Construction Administration
Quality control
Some Open Issues
Automation
Active database functionality
Incorporation of domains and business rules

17. DW Trends Real- time DW Data Management Practices
Cloud Computing and SaaS
In-memory computing and 64-bit computing
Open Source software
Advanced Analytics
Services
Processing Architecture
DW Appliances and similar platforms
New database management systems

19. Applied Research in Visual Analytics
What Technologies Will be the Most Important to you in the Next Three Years?
200 CIO Directors and 20 Business Sponsors replied:
Visualization ranked second highest trend in BI for next three years
I briefly visited the Executive Summit to hear about what trends CIOs and VPs of business intelligence think will have the biggest impact over the next three years. Predictive analytics once again topped the list, but now in the second-highest spot was advanced visualization and discovery*
aRIVA
Applied Research in Visual Analytics
Posted by Cindi Howson Monday, August 23, 2010

20. Overview of current topics in BI
Related topics of Visualization, Dashboards and Agile BI make compelling case for Information Visualization
aRIVA
Applied Research in Visual Analytics

21. References
Elmasri, R. and Navathe, S. Fundamental of database systems, 5th Editions, Addison Wesley 2006
Chaudhari, S, and Dayal, U. “An Overview of Data Warehousing and OLAP Technology”, SIGMOD Record, Vol. 26, No 1, March 1997
Kimball, R. The Data Warehousing Toolkit, Wiley, Inc. 1996
Inmon, W.H. Building the Data Warehouse, Wiley, Inc. 1992
Russon, P. “Next Generation of Data Warehouse Platforms”, TDWI Best Practices Report, Fourth Quarter 2009

22. Important terms Database Data Warehouse LAPs (OLAP, ROLAP, MOLAP) DSS
EIS
Data Mining
Metadata
OLTP
Enterprise-wide data warehouses
Virtual Data warehouses
Data Marts
Star Schema
Snowflake Schema
Fact Constellation
Backflushing
Distributed DW
Federated DW