Data Warehousing and Analytics: Fueling the Data Engine

1. Introduction.- Part I: Star Schema.- 2. Simple Star Schemas.- 3. Creating Facts and Dimensions: More Complex Processes.- Part II: Snowflake and Bridge Tables.- 4. Hierarchies.- 5. Bridge Tables.- 6. Temporal Data Warehousing.- Part III: Advanced Dimension.- 7. Determinant Dimensions.- 8. Junk Dimensions.- 9. Dimension Keys.- 10. One-Attribute Dimensions.- Part IV: Multi-Fact and Multi-Input.- 11. Multi-Fact Star Schemas.- 12. Slicing a Fact.- 13. Multi-Input Operational Databases.- Part V: Data Warehousing Granularity and Evolution.- 14. Data Warehousing Granularity and Levels of Aggregation.- 15. Designing Lowest-Level Star Schemas.- 16. Levels of Aggregation: Adding and Removing Dimensions.- 17. Levels of Aggregation and Bridge Tables.- 18. Active Data Warehousing.- Part VI: OLAP, Business Intelligence, and Data Analytics.- 19. Online Analytical Processing (OLAP).- 20. Pre- and Post-Data Warehousing.- 21. Data Analytics for Data Warehousing.

David Taniar is an Associate Professor in the Faculty of Information Technology, Monash University Australia. He is the Editor-in-Chief of the International Journal of Data Warehousing and Mining. He has published more than 400 papers and books on database technology, including a highly-rated book on High Performance Parallel Databases and Grid Databases. He has graduated more than 25 PhD students in the area of data management.
Wenny Rahayu is a Professor in Computer Science and currently the Dean of the School of Engineering and Mathematical Sciences, La Trobe University, Australia. She graduated with a PhD degree in Computer Science, specializing in Databases from La Trobe University in 2001. Her PhD thesis has been awarded the Best PhD Thesis Award by The Computing Research and Education Association of Australasia (CORE), an association of university departments of computer science in Australia and New Zealand. She has published more than 300 papers and successfully supervised 17 PhD students in databases.