Require ANU B.Sc IT Paper – IV : Data Warehousing Nov Dec 2014 Question Paper ? You can collect this exam paper in text provided as provided below:
University : Acharya Nagarjuna University
QP Code : DSDW31
B.Sc. DEGREE EXAMINATION, NOV./DEC. – 2014
Third Year
Part – II : INFORMATION TECHNOLOGY
Paper – IV : Data Warehousing
Time : 3 Hours Maximum Marks : 80
Answer any five questions
All questions carry equal marks
1) What is a Data Ware House? What are its advantages? How it is different from operational database system?
2) Explain the procedure. How you build and manage Data Ware House program.
3) Discuss in detail about project estimates, project work Breakdown and Critical Path Analysis.
4) Discuss about Data Ware Housing Development Methodologies.
5) Explain about Data Marts and star scheme design.
6) Explain how performance can be achieved in a physical Data Ware House.
7) Discuss in detail about ETL architecture.
8) Discuss about indexing approach available for Data Ware House.
9) Explain Data Ware House process manager.
10) What is Data Mining? Explain the real world application of data mining.
University : Acharya Nagarjuna University
QP Code : DSDW31
B.Sc. DEGREE EXAMINATION, NOV./DEC. – 2014
Third Year
Part – II : INFORMATION TECHNOLOGY
Paper – IV : Data Warehousing
Time : 3 Hours Maximum Marks : 80
Answer any five questions
All questions carry equal marks
1) What is a Data Ware House? What are its advantages? How it is different from operational database system?
2) Explain the procedure. How you build and manage Data Ware House program.
3) Discuss in detail about project estimates, project work Breakdown and Critical Path Analysis.
4) Discuss about Data Ware Housing Development Methodologies.
5) Explain about Data Marts and star scheme design.
6) Explain how performance can be achieved in a physical Data Ware House.
7) Discuss in detail about ETL architecture.
8) Discuss about indexing approach available for Data Ware House.
9) Explain Data Ware House process manager.
10) What is Data Mining? Explain the real world application of data mining.
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