1. Business Intelligence

2. BI Fundamentals Business Transactions Data Bases Data Warehouses
Data Marts
Data Mining

3. Data bases (DB or DBMS)
“A collection of information organized in such a way that a computer program can quickly select desired pieces of data.”
An electronic filing system
Organized by
Fields: a single piece of information
Records: one complete set of fields
Files: a collection of record

4. Data warehouse (DW)
“Contain a wide variety of data that present a coherent picture of business conditions at a single point in time.”
“A database system which contains periodically collected samples or summarized (aggregated) transactional data; e.g., daily totals, or monthly averages”
Typically a compilation of information from multiple transactional databases

5. Data mart
“A database, or collection of databases, designed to help managers make strategic decisions about their business.”
A smaller and more focused form of a data warehouse.
Usually created for a particular department or position
A data mart created as a subset of data warehouse data are referred to as a “dependent data mart”.

6. Data mining
“A class of database applications that look for patterns in data to be used to predict and direct future behavior.”
Increasingly being used by marketers to find consumer data through the web and store purchases.

7. What is BI?
The new technology for understanding the past and predicting the future
A broad category of technologies that allows for
Gathering, storing, accessing and analyzing the data business users make better decisions
Analyzing business performance through data-driven insight
A broad category of applications, which includes the activities of
Decision support systems
Query and reporting
OLAP
Statistical, forecasting and data mining

8. BI vs. AI AI systems make decisions for the users
BI systems help users make the right decisions, based on the available data
However, many BI techniques have roots in AI

9. BI Processes
MDA (Model Driven Architecture)

10. Business Intelligence
PSA (Persistant Staging Area), ODS (Operational Data Store)

11. Data-Information–Knowledge–Decision Making Cycle

12. What does BI seek to find?
Patterns
What kind of patterns?
Sales
Stocks
Anything useful

13. Techniques for Finding Patterns
Statistics
Trends
Correlation (searching for a best fit)

14. Patterns continued Combinatorial Example If-then relationships
If we put chips on sale on a Friday, then we also sell more soda.

15. Leading the Industry Cognos Software BI software company
Used for reporting, analysis, scorecarding, dashboards, business event management, and data integration

16. Cognos Multiple Solutions Industry Department Banking Education
Defense
Government
Department
Executive Management
Finance
Marketing

17. Open Source Tools for BI
ETL (Extract, Transform, Load) tools
OLAP (Online Analytical Processing) servers
OLAP clients
DBMSs (Data Base Management System)

18. ETL Tools Bee CloverETL Octopus
ROLAP (Relational OLAP) oriented ETL tool
CloverETL
ROLAP oriented ETL tool
Implemented in Java and uses JDBC to transfer data
cloveretl.berlios.de
Octopus
Implemented in Java and uses JDBC
octopus.objectweb.org

19. OLAP Servers Bee Lemur Mondrian ROLAP oriented server
Uses mySQL to manage the DB
sourceforge.net/products/bee/
Lemur
HOLAP oriented server
Mondrian
Implemented in Java
Can be used with any DBMS
sourceforge.net/projects/mondrian/

20. OLAP Client Bee Jpivot Web-based, used with Bee OLAP server
Generates pie, bar, chat, etc. (in 2D & 3D)
Export data to Excel, PDF, PNG, Powerpoint, XML
Jpivot
Web-based, used with Mondrian OLAP server
Generates 2D & 3D graphics
Export data to PDF
jvipot.sourceforge.net

21. DBMSs MonetDB MySQL MaxDB Run on Linux, Windows, Mac OS, etc.
monetdb.cwi.nl
MySQL
Run on Linux, Windows
MaxDB
Formely SAP DB (by SAP AG)

22. PostgreSQL www.postgresql.org
Run on Linux, Unix, Windows (versi > 8.0)

23. PALO OLAP Palo OLAP Server Palo ETL Server Palo OLAP Client
Open source MOLAP server
be installed locally or in a company network
Palo ETL Server
enables the efficient extraction of mass data from heterogeneous data sources, ie. all common relational database systems and flat files
Palo OLAP Client
Two versions: Palo Client and Palo Web Client

24. Data Mining Softwares Open sources Commercials
Borgelt data mining suite
Gnome data mine
Weka
RapidMiner
Commercials
See5 (Rulequest)
Clementine (SPSS)
Enterprise Miner (SAS)
GhostMiner (Fujitsu)
Statistica Data Miner (StatSoft)
Oracle Data Miner (Oracle)

25. Borgelt Data Mining Suite
Tasks:
Association: apriori, eclat
Classification: bayesian networks, decision trees, naive bayes
Regression: neural networks
Clustering: self-organizing maps (SOM)
Platforms: Linux, Unix, MS Windows
Website:

26. Genome Data Mine Tasks: Platforms: Linux, Unix, MS Windows Website:
Association: apriori
Classification: decision trees
Platforms: Linux, Unix, MS Windows
Website:
Owner: Togaware, Canberra, Australia.

27. WEKA Tasks: Platforms: Linux, Unix, MS Windows Website:
Association: apriori
Classification: decision trees, support vector machines, conjunctive rules
Clustering: k-means
Platforms: Linux, Unix, MS Windows
Website:
Owner: University of Waikato, Hamilton, New Zealand

28. RapidMiner http://rapid-i.com/
The world-leading open-source system for knowledge discovery and data mining
Multiplaftorm: implemented in Java
Supports about 400 operators data mining

29. Who uses BI? Businesses The Government ?
What are some ethical implications of the use of BI?