Pune University MCA Question Papers / Previous year question papers of Pune University / MCA Advanced Databases Question Paper
Total No of
Questions: [12] SEAT
NO. :
[Total
No. of Pages : 02]
[4366]-
503
TYMCA
(Engg. Faculty)
ADVANCED
DATABASES
(Semester
- V) (2008 Pattern) (710903)
MAY
2013 EXAMINATIONS
[Time:
3 Hours]
[Max. Marks : 70]
Instructions to the candidates:
1) Answers to the two sections should be written
in separate books.
2) Neat diagrams must be drawn wherever necessary.
3) Assume Suitable data if necessary.
SECTION
I
Q1) a) With suitable diagrams explain the steps in
query processing. [5]
b) Explain the external sort merge algorithm with
suitable example. [6]
OR
Q2) a) What are the measures of query cost? [5]
b) Explain the different ways of executing
pipelines. [6]
Q3) a) Explain Transaction Server Process
Structure. [6]
b) What are the implementation issues of
distributed systems. [6]
OR
Q4) a) Explain Speed up & Scale up. [6]
b) Explain centralized and client server database
architecture [6]
Q5) a) Explain object identity and reference type?
[6]
b) Why OODBMS is required Differentiate between
DBMS, RDBMS and OODBMS. [6]
OR
Q6) a) Explain Array and Multiset in SQL with
example. [6]
b) Explain persistent C++ system. [6]
SECTION
II
Q7) a) While analyzing the data,
it was found that many tuples have no recorded values for several attributes.
How this problem of missing values can be solved? [6]
b) Explain snowflake schema for multidimensional
database. [6]
OR
Q8) a) Explain in brief OLAP. What are the
possible operations on cube? [6]
b) Explain star schema for multidimensional
database. [6]
Q9) a) Form clusters using
clustering K-Means algorithm. Use appropriate distance formula. [8]
|
RID
|
Age
|
Years of
Service
|
|
1
|
30
|
5
|
|
2
|
50
|
25
|
|
3
|
50
|
15
|
|
4
|
25
|
5
|
|
5
|
30
|
10
|
|
6
|
55
|
25
|
b) Explain outlier analysis [4]
OR
Q10) a) Find frequently occurred item using
apriori algorithm. [8]
|
ITD
|
ITEM
|
|
100
|
1,3,4
|
|
200
|
2,3,5
|
|
300
|
1,2,3,5
|
|
400
|
2,5
|
b) Explain descriptive & predictive data
mining. [4]
Q11) a) Describe the ranking using TF-IDF. [8]
b) Define the following terms. [3]
1) Hub 2) Authority 3) Web crawler
OR
Q12) a) Describe the popularity ranking. [8]
b) Define the following terms- [3]
1) Ontology 2) Search engine spamming 3) False
positive
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