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Posted Date: 16 May 2009      Posted By: Ashis Dubey      Member Level: Diamond

2008 The Institution of Engineers,India A.M.I.E.T.E Information Technology AT19 : DATA WAREHOUSING AND DATA MINING (DEC 08) University Question paper



Course: A.M.I.E.T.E Information Technology   University: The Institution of Engineers,India




Code: AT19 Subject: DATA WAREHOUSING AND DATA MINING
Time: 3 Hours Max. Marks: 100

NOTE: There are 9 Questions in all.
· Question 1 is compulsory and carries 20 marks. Answer to Q. 1. must be written in the space provided for it in the answer book supplied and nowhere else.
· Out of the remaining EIGHT Questions answer any FIVE Questions. Each question carries 16 marks.
· Any required data not explicitly given, may be suitably assumed and stated.


Q.1 Choose the correct or best alternative in the following: (2x10)

a. OLTP stands for

(A) Online Transaction Processing System.
(B) Offline Transaction Processing System.
(C) Online Transaction Systems.
(D) Online Table Processing Systems.

b. PCA is a technique used for

(A) Mining Patterns.
(B) Compressing Data.
(C) Integrating Data.
(D) Cleaning Data.

c. Data Mining includes

(A) Analyzing large volumes of data to discover interesting associations or patterns
(B) Querying a large data warehouse to uncover undiscovered facts
(C) Very complex SQL query operations
(D) Slicing and dicing until you uncover interesting details

d. The normalized schema is called

(A) Star schema
(B) Snowflake schema
(C) Multidimensional schema
(D) Cube

e. Metadata is

(A) Data out of main data
(B) Data about data
(C) Separating primary and secondary data
(D) Partitioning of data

f. Types of dimensions in a spatial data cube are

(A) 0 (B) 1
(C) 2 (D) 3
g. CMSM stands for

(A) Combined Media Storage management
(B) Cross Media Storage Management
(C) Combined Management of Stored Media
(D) None of the above

h. Star join technique applies to

(A) World of data-marts (B) World of data-warehouses
(C) World of database (D) None of the above

i. Following represents structured data

(A) Emails
(B) Portable Document Format (.pdf) files
(C) Microsoft PowerPoint (.ppt) files
(D) Standard DBMSs

j. Query and reporting tools are most appropriate for

(A) Controlled predictable query environments
(B) Adhoc reporting environments
(C) Complex multifaceted business query application
(D) Discovering mode applications




Answer any FIVE Questions out of EIGHT Questions.
Each question carries 16 marks.

Q2. a. Differentiate between Operational database systems and Data warehouse. (8)

b. Describe Three-Tier data warehouse architecture. (8)

Q3. a. What is Event Mapping? Explain with the help of an example. (8)

b. What is Executive Information Systems (EIS)? How EIS is related to data warehousing? (8)

Q4. a. Discuss two issues related to use and storage of external data in the data warehouse. (8)

b. What is the relationship between the data model and external data? (8)

Q5. a. What is data preprocessing? Briefly describe four data preprocessing techniques. (8)

b. (i) Minimum and maximum values for an attribute income are given as $12,000 and $98,000 respectively. Income has to be mapped to range [0.0, 1.0]. Using min-max normalization transform a value of $73,600 for income. (4)
(ii) Mean and standard deviation of the values for an attribute income are given as $54,000 and $16,000 respectively. Using z-score normalization transform a value of $73,600 for income. (4)

Q6. a. What are technological challenges in bringing the system-of-record data into the data warehouse? (8)

b. Give four reasons why methodologies are disappointing. (8)

Q7. a. Discuss in brief four levels in architected environment. (8)

b. Discuss in detail-Hierarchical & Density-based methods in clustering. (8)

Q8. a. Describe 3-4-5 rule of segmentation with the help of an example. (8)

b. What are multidimensional cubes? What are advantages of multidimensional cubes? (8)

Q9. Write short notes on any FOUR:-

(i) Drill-Down analysis
(ii) Multidimensional data model
(iii) Archiving external data
(iv) Feedback loop
(v) Machine learning (4 x 4 = 16)





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