Previous Year Very Short Questions of DWM (MSC-IT 4th)

DATA WAREHOUSING AND DATA MINING

Previous year question paper with solutions for DATA WAREHOUSING AND DATA MINING

Our website provides solved previous year question paper for DATA WAREHOUSING AND DATA MINING . Doing preparation from the previous year question paper helps you to get good marks in exams. From our DWM question paper bank, students can download solved previous year question paper. The solutions to these previous year question paper are very easy to understand.

These Questions are downloaded from www.brpaper.com You can also download previous years question papers of 10th and 12th (PSEB & CBSE), B-Tech, Diploma, BBA, BCA, MBA, MCA, M-Tech, PGDCA, B-Com, BSc-IT, MSC-IT.

Print this page
  1. Write detailed note on three tier data warehouse architecture.
  2. Write note on various Pre-Processing Tasks.
  3. What is meant by Big Data? Are Data Warehouses and Big Data Interrelated? If yes, Justify.
  4. What is Star Schema? What are the Potential Problems with Star Schema? Explain with examples.
  5. Temporal Support for levels.
  6. Conceptual Models for Temporal Data Warehouses.
  7. Need of Synchronization and Relationships in Temporal Data Warehouse
  8. Fact Relations and Measures.
  9. Explain the Three-Tier Data Warehouse Architecture
  10. What is spatial data warehouse? Explain the architecture of spatial systems.
  11. Discuss the conceptual models for temporal data warehouse.
  12. Explain temporal hierarchies
  13. Explain temporal measures.
  14. What is Bayesian classification? Explain the use of naive Bayesian classification.
  15. What is back propagation? Write an algorithm for classification by back propagation.
  16. Explain density based methods for clustering.
  17. Explain partitioning methods for clustering.
  18. Discuss the Partitioning method for Cluster Analysis with example.
  19. Discuss the Multiple Regression used for Prediction with suitable example.
  20. With relevant examples, discuss the various types of Data in Cluster Analysis.