MU Computer Engineering (Semester 6)
Data Warehouse And Data Mining
December 2012
Total marks: --
Total time: --
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

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 M
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.
Transactions Items
T1 Bread, Jelly, Butter
T2 Bread, Butter
T3 Bread, Milk, Butter
T4 Coke, Bread
T5 Coke, Milk
10 M

Define the following terms by giving example:-
2 (a) Factless fact tables
5 M
2 (b) Snowflake schema
5 M
2 (c) Web Structure Mining
5 M
2 (d) Classification
5 M

3 (a) Explain the ETL cycle for a data warehouse in detail.
10 M
3 (b) Give five examples of application that can use Clustering. Describe any one clustering algorithm with the help of example.
10 M

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 M
4 (b) Explain KDD process in detail.
10 M

5 (a) What do you mean by Web Mining? Explain any one web mining algorithm.
10 M
5 (b) Describe different feature of a web enabled data warehouse. Give two example of application where such a system would be used.
10 M

6 (a) Expain Spatial and temporal Data mining.
10 M
6 (b) Explain role for Meta data in Data Warehouse. Illustrate with example.
10 M

Write short notes on:-
7 (a) DMQL
10 M
7 (b) Visualization techniques for Data warehousing and mining
10 M

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