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Dw Note for Finance

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Introduction to Data Warehousing and Business Intelligence
Slides kindly borrowed from the course “Data Warehousing and Machine Learning” Aalborg University, Denmark Christian S. Jensen Torben Bach Pedersen Christian Thomsen {csj,tbp,chr}@cs.aau.dk

Course Structure
• Business intelligence




Extract knowledge from large amounts of data collected in a modern enterprise Data warehousing, machine learning Acquire theoretical background in lectures and literature studies Obtain practical experience on (industrial) tools in practical exercises
Data warehousing: construction of a database with only data analysis purpose



Purpose
 

Business Intelligence (BI)

Machine learning: find patterns automatically in databases
2

•1

Literature
• Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010 • Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009 • Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008 • The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998 • The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002
3

Overview
• • • • Why Business Intelligence? Data analysis problems Data Warehouse (DW) introduction DW topics
  

Multidimensional modeling ETL Performance optimization

4

•2

What is Business Intelligence (BI)?
• From Encyclopedia of Database Systems: “[BI] refers to a set of tools and techniques that enable a company to transform its business data into timely and accurate information for the decisional process, to be made available to the right persons in the most suitable form.”

5

What is Business Intelligence (BI)?
• BI is different from Artificial Intelligence (AI)
 

AI systems make decisions for

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