Data warehouse and OLAP technology for data mining Questions

1. Define a data warehouse and explain its key features. Explain how a data
warehouse is different from a database?
2. Explain about data cubes. Comment about the suitability of data cubes for
data warehouses and data marts in terms of different schemas.
3. Explain about data cube definition in a query language like DMQL. 40 to 45
4. What is meant by a data cube measure? What are the different categories
of measures? Illustrate how measures are defined in a query language like
DMQL?
5. Taking suitable examples write an introduction about concept hierarchies,
lattices, schema hierarchies
6. Using a suitable example, explain about different OLAP operations. 61 to 69
7. Write a short note on starnet query model for multidimensional tables 70 to 72
8. Explain the views and the steps in the design of a data warehouse. 74 to 82
9. Explain the 3‐tier architecture of a data warehouse with reference to data
warehouses, data marts, and virtual warehouses.
10. Explain about ROLAP, MOLAP, HOLAP, and specialized SQL servers 92 to 99
11. Explain about efficient computation of data cubes as part of data
warehouse implementation
12. Explain the bitmap indexing and join indexing of OLAP data 126 to 133
13. Explain the steps in efficient query processing in case of materialized views 134 to
14. Explain about metadata 141 to 144
15. Write a short note on data warehouse back‐end tools 145
16. Explain about discovery‐driven exploration of data warehouses 146 to 162
17. Taking suitable examples, explain about complex aggregation at multiple
granularities
18. Explain about the various tools used in data warehouse for knowledge
discovery
19. Discuss the reasons for the importance of OLAM 188 to 191
20. Explain about the architecture of OLAM 191 to 194