DATA WAREHOUSE AND DATA MINING (DWDM) MAY 2013 COMPUTER SCIENCE SEMESTER 6
Con. 9998-13. GS-1369
(3 Hours) [Total Marks : 100]
Note: 1. Question 1 is compulsory
2. Answer any 4 out of the remaining questions.
3. Answers to sub questions must be written together.
| Q1. (a) | What are differences between Data Warehouse and Data Mart ? | (05) |
| (b) | For Supermarket Chain consider the following dimensions, namely Product, store, | (05) |
| time , promotion. The schema contains a central fact table, sales facts with three | ||
| measures unit_sales, dollars_sales and dollar_cost. Design star schema for this | ||
| application. | ||
| (c) | Calculate the maximum number of base fact table records for warehouse with the | (05) |
| following values given below : | ||
| Time period: 5 years | ||
| Store: 300 stores reporting daily sales | ||
| product: 40,000 products in each store (about 4000 sell in each store daily) | ||
| (d) | Illustrate how the supermarket can use clustering methods to improve sales. | (05) |
Q2. | Define the following terms by giving examples | (20) |
| (a) Factless fact tables | ||
| (b) Snowflake Schema | ||
| (c) Web Structure Mining | ||
| (d) Concept Hierarchy | ||
Q3. (a) | Apply Agglomerative Hierarchical Clustering and draw single link and average | (10) |
link dendrogram for the following distance matrix. | ||
| (b) | Explain the Page Rank technique with algorithm. | (10) |
Q4. (a) | Consider a data warehouse for a hospital, where there are three dimensions: | (10) |
| (1) Doctor (2) Patient (3) Time; and two measures: (1) Count & (2) Fees; | ||
| For this example create a Olap cube and describe the following OLAP operations: | ||
| (b) | Consider the following transaction database: | (10) |
| TID Items | ||
| 01 A, B, C, D | ||
| 02 A, B, C, D, E, G | ||
| 03 A, C, G, H, K | ||
| 04 B, C, D, E, K | ||
| 05 D, E, F, H, L 06 A, B, C, D, L | ||
| 07 B, I, E, K, L | ||
| 07 B, I, E, K, L | ||
| 08 A, B, D, E, K | ||
| 09 A, E, F, H, L | ||
| 10 B, C, D, F | ||
Apply the apriori algorithm with minimum support of 30% and minimum confidence of | ||
| 70%, and find all the associtaion rules in the data set. | ||
| Q5. (a) | A simple example from the stock market involving only discrete ranges has | |
Profit as categorical attribute, with values {up, down}. and the training data is: | ||
| Apply the decision tree algorithm and show the generated rules. | (10) | |
| (b) | Describe the steps of the ETL (Extract - Transform - Load) cycle. | (10) |
Q6. (a) | Define multidimensional and multilevel association mining. | (10) |
| (b) | Explain the role of Meta data in a data warehouse. | (10) |
Q.7 | Write detailed notes on : | (20) |
| (a) Data Warehouse Architecture | ||
| (b) K-Means Clustering |
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