Data Warehouse And Data Mining - Dec 2012
Computer Engineering (Semester 6)
TOTAL MARKS: 100
TOTAL TIME: 3 HOURS (1) Question 1 is compulsory.
(2) Attempt any four from the remaining questions.
(3) Assume data wherever required.
(4) Figures to the right indicate full marks. 1 (a) Consider the following database for a chain of bookstores -
BOOKS (Booknum, Primary_author,Topic, Total_stock, price)
STOCK (Storenum, Booknum, Qty)
With respect to the above business scenario, answer the following questions. Clearly state any reasonable assumptions you make.
(a) Design an information package diagram.
(b) Design a star schema for the data warehouse clearly identifying the Fact table(s), Dimension table(s), their attributes and measures.(10 marks) 1 (b) Consider the 5 transactions given below. If minimum support is 30% and minimum confidence is 80%, determines the frequent item sets and association rules using the apriori algorithm.
|T1||Bread, Jelly, Butter|
|T3||Bread, Milk, Butter|
Define the following terms by giving example:-
2 (a) Factless fact tables(5 marks)
2 (b) Snowflake schema(5 marks)
2 (c) Web Structure Mining(5 marks)
2 (d) Classification(5 marks)
3 (a) Explain the ETL cycle for a data warehouse in detail.(10 marks)
3 (b) Give five examples of application that can use Clustering. Describe any one clustering algorithm with the help of example. (10 marks)
4 (a) Consider a data warehouse storing sales details of various goods sales, and the time of the sale. Using this example describe the following OLAP operations
1) Slice 2) Dice 3) Rollup 4) Drill down(10 marks) 4 (b) Explain KDD process in detail.(10 marks) 5 (a) What do you mean by Web Mining? Explain any one web mining algorithm.(10 marks) 5 (b) Describe different feature of a web enabled data warehouse. Give two example of application where such a system would be used.(10 marks) 6 (a) Expain Spatial and temporal Data mining.(10 marks) 6 (b) Explain role for Meta data in Data Warehouse. Illustrate with example.(10 marks)
Write short notes on:-
7 (a) DMQL(10 marks) 7 (b) Visualization techniques for Data warehousing and mining (10 marks)