18-06-2013, 04:51 PM
Question Bank DM&DW(ECS-075)
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1. Explain 2 Tier and 3 tier architecture of DWH.
2. What do you mean by cleaning of data. Explain the important types of data cleaning.
3. Describe the basic phases of KDD Process.
4. Describe the main features of DWH.
5. What do you mean by granularity. What is Partitioning?
6. What are Data Marts. How they are different from Data warehouses?
7. Describe in brief Data Mining and Data Mining functionalities.
8. Describe important types of difficulties in data mining process.
9. Describe in brief the process of Data Integration and Transformation.
10. Write and explain characterstics of operational data.
11. Describe the Apriori algorithm for FIM(Frequent Intemset Mining) and verify it through a
suitable example.
12. What is clustering? How it is different from classification?
13. Explain different approaches for clustering.
14. Briefly explain important approaches to build the data warehouse.
15. Define and describe the basic similarities and differences among ROLAP, MOLAP, HOLAP and
DOLAP.
16. Describe the various OLAP Operations, Explain how query performance can be improved by
cascading the operations?
17. Describe Data Mining Interface.
18. Describe and Explain Testing of Data Warehouse.
19. Explain Aggregation. How does the OLAP handle the aggregation.
20. Describe star, snowflake, fact constellation schemas for multidimensional data models with
suitable example.
21. Discuss the differences between data warehouse and database systems.
22. Describe the neural network. How is neural network helpful in classification.
23. What is Decision Tree? Explain the classification by decision tree inductionwith example.
24. Explain the mining multidimensional association rules from relational databases and data
warehouses.
25. Explain OLAP functions and tools in brief. What are the main features of OLAP servers.
26. Explain Multidimensional Data Model.
27. Differentiate between OLAP and OLTP.
28. What are different types of classification techniques. Discuss issues regarding classification and
prediction.
29. Define the terms generalization and analytical characterization with example.
30. What do you understand by Data Visualization? What are the common techniques of Data
Visualization? Describe characterstics of good visualization.