Data Mining & Business Intelligence

(a) Explain five characteristics features of a Data Warehouse in detail. 

Explain the following term:

(iii) Slice and Dice.

A data warehouse can be modeled by either a star schema or a snowflake schema. Briefly describe the similarities and the difference of the two models, and then analyze their advantages and disadvantages with regard to one another. Give your opinion of which might be more empirically useful and state the reasons behind your answer. 

Explain the following term:

(ii) Roll Up

Explain the following term:

(i) Drill Down

Answer  following question in brief:

(j) What is the difference between ETL and ELT?

Answer  following question in brief:

i) What do you mean by interesting pattern?

Answer  following question in brief:

(h) Differentiate between Data Mining and Data warehousing.

Answer  following question in brief:

(g) Why do you need a staging area?

Answer  following question in brief:

(f) What are the steps of doing dimensional modeling? 

Answer  following question in brief:

(c) What are the advantages of dimensional DW compared to normalized DW?

Answer  following question in brief:

(d) Define Load Manager and it's functions.

Answer  following question in brief:

(c) What is Star Schema?

Answer  following question in brief:

(b) What are the benefits of Data Warehouse?

Answer  following question in brief:

(a) What is meant by Data Analytics?

(a) What are the various models of OLAP? Explain each in detail.

(b) A Dimension Table is Wide; the Fact Table is Deep, explain. What is Fact Less Fact Table? 

(a) Why is the ER modeling technique not suitable for the Data Warehouse? How is Dimensional modeling different?

(b) What are additive, semi-additive and non-additive measures? Explain· with examples. 

(a) How does OLAP impact the data warehouse? Explain its benefits and limitations.

(b) What is clustering? How is it different from classification?

(a) Write an algorithm for K-nearest neighbor classification given k and n, the number of attributes describing each sample.

(b) Describe challenges to data mining regarding data mining inethodology and user interaction issues. 

(a) What are Outliers? Explain Outlier analysis.

(b) What are the different data extraction and data cleaning techniques?Explain.