Code Hall Ticket Number:

RM-21
No:M21CS4203A
MVR COLLEGE OF ENGINEERING AND TECHNOLOGY
(UGC AUTONOMOUS)
M.TECH II SEMESTER REGULAR SUPPLY EXAMINATIONS, JULY -2024
ADVANCED DATABASES AND MINING
Time :3 hours Max.Marks: 75
Answer any five Questions one Question from Each Unit,All Questions Carry Equal Marks
UNIT -I
1 A) Consider the relation schema R(A, B, C, D, E, F, G, H, I, J) with set of 7M
functional dependencies F={ABC, ADE, BF, FGH, DIJ}.
The relation R is decomposed into R1={A,B,C,D,E}, R2={B,F,G,H} and
R3={D,I,J}.
Check whether the decomposition satisfies:
(i) Dependency preservation and
{ii) Lossless join property.
B) (i) Define BCNF. 8M
(ii) Consider a relation R(A,B,C,D,E) with the following functional
dependencies: F={AB → C, BC → D, CD → E, DE → A}.
Compute the key for the relation R and check whether the above relation
satisfies BCNF or not?
OR
2 A) Consider the following relational schemas 7M
Student(Roll_No, Name, DoB)
Dept(Dept_No, Dname, Location)
Project( Proj_No, Dept_No, Roll_No, PName)
Express the following using relatioanl algebra and tuple calculus
i. Find the names of the students who have selected any project offered by the
‘IT’ department.
ii. Find the names of the students who born on 01-FEB-2004
B) i. Define Partial functional dependeny and transitive dependency. 8M
ii. Consider the relation R(A,B,C,D,E,F,G,H,I,J) with F={ABC, ADE,
BF, FGH, DIJ}.
a. Find the key.
b. Check whether the above relation satisfies 3NF or not? If not,
decompose it into 3NF.
UNIT -II
3 A) Consider the following schedule S involving five transactions T1, T2, T3, T4 7M
and T5:

R(X) denotes read operation on data item X by transaction Ti.

W(X) denotes write operation on data item X by transaction Ti.
i. Check whether the above schedule is conflict serializable or not?
ii. If it is conflict serializable, give its equivalent serial schedule.
B) Draw transaction state diagram. 8M
Explain various properties of the transaction.

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 OR
4 A) What is SQL injection attack? Explain with an example 7M
B) i. Consider the following transactions. 8M
T1 T2
R(X)
W(X)
R(X)
W(X)
Assume that the transaction T1 started at 10.00am and Transaction T2 started
at 10.01am. Write the Read and Write timestamps of X after execution of T1
and T2.
ii. Write timestamp ordering algorithm.
UNIT -III
5 A) Compare Star, Snowflake and fact constellation schemas. 8M
B) How OLAP differs with OLTP? Explain. 7M
OR
6 A) Normalize 200,300,400,600,1000 using 8M
i. min-max normalization (Assume min=0 and max=1)
ii. z-score normalization
B) What is concept hierarchy? Explain the process for data discretization using 7M
natural partitioning?
UNIT -IV
7 A) Generate the association rules from the given dataset. Assume support 8M
count=2 and confidence=60%.

B) Consider the following table: 7M

Let the minimum support be 40%. Let the minimum confidence be 60%.
i. Check whether A=>B Strong association rule.
ii. Find the correlation between A,B.

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 OR
8 A) Generate all frequent itemsets from the transaction database given below 8M
using FP-growth algorithm. (minimum support= 2)
TID List of items
T1 B,A,T
T2 A,C
T3 A,S
T4 B,A,C
T5 B,S
T6 A,S
T7 B,S
T8 B,A,S,T
T9 B,A,S
B) “Strong association rules are not always interesting”. Justify this statement 7M
with an example.
UNIT -V
9 A) Write the algorithm for decision tree induction. Construct the decision tree 7M
using the following data set.

Case Outlook Temperature Humidity Windy Play

1 sunny hot high FALSE no

2 sunny hot high TRUE no

3 overcast hot high FALSE yes

4 rainy mild high FALSE yes

5 rainy cool normal FALSE yes

6 rainy cool normal TRUE no

7 overcast cool normal TRUE yes

8 sunny mild high FALSE no

9 sunny cool normal FALSE yes

10 rainy mild normal FALSE yes

11 sunny mild normal TRUE Yes

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 12 overcast mild high TRUE yes

13 overcast hot normal FALSE yes

14 rainy mild high TRUE no

B) Compare the performance of classifiers by using boosting and bagging 8M

techniques.

OR
10 A) Suppose that the data mining task is to cluster the following eight points 7M
(with (x, y) representing location) into three clusters:

A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5), B3(6, 4), C1(1, 2), C2(4, 9):

The distance function is Euclidean distance. Suppose initially we assign A1,

B1, and C1 as the center of each cluster, respectively. Use the k-means
algorithm to show final clusters.
B) Explain the role of authoritative and hub pages in web linkage structures 8M
mining.

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