Advanced Databases - Pune University MCA Question Paper - MAY 2013

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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]

Answer:

Descriptive data mining - It is the idea of using the data to identify the relationships. Find human-interpretable patterns that describe the data. Clustering, association rule mining and sequential pattern discovery are some of the descriptive approaches.

Predictive data mining - It is the idea of using data to make a prediction. It uses some variables to predict unknown or future values of other variables. Classification, and regression are some of the predictive approaches.  

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

Answer:

False positive: A false positive is where you receive a positive result for a test, when you should have received a negative results. It’s sometimes called a “false alarm” or “false positive error.” It’s usually used in the medical field, but it can also apply to other arenas (like software testing). Continue reading.

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