Download Data warehousing and Mining Importance Eight times 1. Explain

Data warehousing and Mining Importance
Eight times
1. Explain data mining as a step in the process of knowledge discovery. Give architecture of a typical data
mining system. (m05, d05, m06, d06, m07, d07, m08, d09)
Seven times
1. Explain ETL of data warehousing in detail. (m06, d06, m07, d07, m08, d08, m09)
Six times
1. What are the types of OLAP servers? Explain the different operation OLAP with suitable example. (m06, d06,
d07, m08, d08, m09)
2. Describe DMQL in detail and explain with an example. (d05, d06, m08, d08, m09, d09)
Five times
1. What is web structure mining? Give the page rank and HITS algorithm used in search engine. (m05, d06,
m07, d07, d09)
Four times
1. Comparison between OLTP and OLAP. (m05, m06, d07, d08)
2. How are top-down and bottom up approaches for building data warehouse different? Discuss the merits
and limitations of each approach. Also discuss the practical approach. (d06, m08, m09, d09)
3. Describe features of web enabled data warehouse. Why is data security a major concern for web-enabled
data warehouse? (d06, m08, m09, d09)
4. Write a detailed note on data warehouse project planning and management. (m05, m06, d06, d07)
5. Why metadata is important? How to provide metadata? (m06, m07, d07, d08)
6. What id MDDB? What types of business requirements determine use of MDDB in data warehouse? (d06,
d07, m09, d09)
7. Define classification. Explain K-nearest neighbour classification algorithm. (d05, d07, m08, d09)
8. Explain K-mean clustering algorithm. Suppose the data for clustering is {1, 3, 5, 15, 23, 11, 25}. Consider K=2,
cluster the given data using above algorithm? (d06, d07, m08, d08)
9. Explain the different algorithm for spatial mining. (d05, m06, d06, m09)
10. What are the techniques and applications of data mining? (m06, d07, m09, d09)
11. SN on trends and applications in DM. (d05, d07, m08, m09)
12. SN on temporal mining. (d07, m08, m09, d09)
Thrice
1. Define Data warehouse with features. Explain the architecture of data warehouse with suitable block
diagram. (m06, d07, d08)
2. State the key issues to be considered while planning for data warehouse. Explain any 4 of them. (m08, d08,
m09)
3. Give information package for recording information requirement for “Hotel Occupancy” considering
dimensions like time, hotel etc. Design star schema and snowflake schema from the information package.
(d05, m08, m09)
4. What is association rule? Describe the suitable tech. using FP trees of mining frequent pattern. (And explain
apriori algo. With e.g.) (m06, m08, m09)
5.
(m05, d06, d07)
6. SN on different classification algorithm in DM. (m06, d06, d07)
7. Explain the different methods of visualization of suitable of suitable e.g. (m06, d07, d08)
8. List and describe five primitives for specifying data mining task. (d05, m08, d08)
9. SN on web mining. (d05, m06, d08)
Twice
1. What is Data warehouse? Why is it needed? Is it just another Data base management system? Compare and
contrast with operational system. (d05, d09)
2. Differentiate between star schema, snowflake schema and fact constellation. (d06, d07)
3. Define factless fact tables giving examples. (m07, m09)
4. Explain the different clustering algo applicable to DM. (m06, m07)
5. What is Web mining? Explain web content mining with reference to crawlers, harvest system, virtual web
view and personalization. (m05, m09)
6. SN on spatial clustering algorithm. (m05, d08)
7. SN on spatial mining. (d05, d09)
8. What is visualization? How can you mine descriptive statistical measure in large databases? (m05, m08)
9. What is visualization? Explain the different technique used for visualization for data warehouse and mining?
(d06, m07)
10. Explain the general trend in data warehousing? (m06, d08)
11. All Electronics company have sales department. Sales consider four dimensions namely time, item, branch
and location. The schema contains a central fact tables sales with two measures dollars sold and unit sold.
(i) Define star schema and Snowflake schema for above case using DMQL?
(ii) Design star schema and Snowflake schema for same.
(m06, d08)
12.
(m05, m09)