Deemed University 2010 M.Tech Information Technology University: Lingayas University Term: III Title of the : Data Warehousing & Data Mining - Question Paper

Roll No. ..

 

Lingayas University, Faridabad

M.Tech (Part-Time) Information Technology

Term-III Examination May, 2010

Data Warehousing & Data Mining (IT-505)

[Time: 3 Hours] [Max. Marks: 100]

 

Before answering the question, candidate should ensure that they have been supplied the correct and complete question paper. No complaint in this regard, will be entertained after examination.

 

Note: Question No. 1 Section A is compulsory. Attempt any two questions from Section B and any two questions from Section C. In all attempt five questions.

Section A

Q-1 Answer the following questions: (4x5=20)

(i) How Operational Data Store differs from Information Data Store?

(ii) What are the different types of Data Marts?

(iii) Define the term Data Mining. List any five Data Mining applications.

(iv) List and explain different type of Data Warehouses.

Section B

Q-2. (a) How a Multi-dimensional database model is realized in a Data Warehouse environment? Discuss. (15)

(b) Explain Star Schema with suitable examples. (05)

Q-3. Discuss in detail the 2-tier and 3-tier architecture of Data Warehouse along with its all the tier. (20)

Q-4. What is the relation between Data Warehouse and Data Mining? How outliers become important in our analysis and forecasting? Discuss with suitable examples. (20)

 

Section C

Q-5. Distributed Data Warehouse has its own limitations and advantages. Discuss this fact along with its various types of design issues. (20)

Q-6. Discuss the role of Data Acquisition, Clean up and Transformation tools in a Data Warehouse. Briefly comment on the role of Meta Data in a Data Warehouse. (20)

Q-7. What are the various types of Data Mining techniques? Explain in detail the concept of clustering and its various types and utilities. Discuss K-means algorithm in detail. (20)

Q-8. How classifiers help in Data Mining? Name the different types of classifiers. Explain the concept of SVM and decision tree in detail. Name the other types of classifiers. (20)

Roll No. ..

 

Lingayas University, Faridabad

M.Tech (Part-Time) Information Technology

Term-III Examination May, 2010

Data Warehousing & Data Mining (IT-505)

[Time: 3 Hours] [Max. Marks: 100]

 

Before answering the question, candidate should ensure that they have been supplied the correct and complete question paper. No complaint in this regard, will be entertained after examination.

 

Note: Question No. 1 Section A is compulsory. Attempt any two questions from Section B and any two questions from Section C. In all attempt five questions.

Section A

Q-1 Answer the following questions: (4x5=20)

(i) How Operational Data Store differs from Information Data Store?

(ii) What are the different types of Data Marts?

(iii) Define the term Data Mining. List any five Data Mining applications.

(iv) List and explain different type of Data Warehouses.

Section B

Q-2. (a) How a Multi-dimensional database model is realized in a Data Warehouse environment? Discuss. (15)

(b) Explain Star Schema with suitable examples. (05)

Q-3. Discuss in detail the 2-tier and 3-tier architecture of Data Warehouse along with its all the tier. (20)

Q-4. What is the relation between Data Warehouse and Data Mining? How outliers become important in our analysis and forecasting? Discuss with suitable examples. (20)

 

Section C

Q-5. Distributed Data Warehouse has its own limitations and advantages. Discuss this fact along with its various types of design issues. (20)

Q-6. Discuss the role of Data Acquisition, Clean up and Transformation tools in a Data Warehouse. Briefly comment on the role of Meta Data in a Data Warehouse. (20)

Q-7. What are the various types of Data Mining techniques? Explain in detail the concept of clustering and its various types and utilities. Discuss K-means algorithm in detail. (20)

Q-8. How classifiers help in Data Mining? Name the different types of classifiers. Explain the concept of SVM and decision tree in detail. Name the other types of classifiers. (20)