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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Advanced Databases Pune University Question Paper Answers 2013

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[Total No. of Questions: 12]                                     [Total No. of Printed Pages : ]

UNIVERSITY OF PUNE

[4364]-777

B. E. (Computer Engg.)(SEM-II) Examination - 2013

Advanced Databases (2008 Course)

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

Instructions:

1 Answer any three questions from each section.

2 Answers to the two sections should be written in separate answer-books.

3 Neat diagrams must be drawn wherever necessary.

4 Black figures to the right indicate full marks.

5 Use of logarithmic tables, slide rule, Mollier charts, electronic pocket calculator and steam tables is allowed.

6 Assume suitable data, if necessary.

SECTION -I

Q.1 A) For each of the three partitioning techniques, namely round-robin, hash partitioning, and range partitioning, give an example of a query for which that partitioning technique would provide the fastest response. [6]

B) Write a short note on Parallel query optimization. [5]

C) What is interquery parallelism? Explain cache coherency problem and protocol available to guarantee cache coherency. [6]

Answer:

It is a form of parallelism where many different Queries or Transactions are executed in parallel with one another on many processors.

 

OR

Q.2 A) Explain parallel External sort-Merge with example. [6]

B) Explain interoperation parallelism suitable example. [6]

C) What factor could result in skew when a relation is partitioned on one of its attributes by Hash partitioning and Range partitioning? In each case, what can be done to reduce the skew? [5]

Q. 3 A) If we are to ensure atomicity, all the sites in which a transaction T executed must agree on the final outcome of the execution T must either commit at all sites, or it must abort at all sites. Describe the techniques or protocol used to ensure this property in detail. [7] ALTERNATE LINK

B) Explain semi-join strategy along with example. [6]

C) Write short note on LDAP. [4]

OR

Q. 4 A) Explain deadlock handling with respect to distributed database. [6]

B) Explain the difference between data replication in a distributed system and the maintenance of a remote backup site. [2]

C) What are the different approaches to store a relation in the distributed database. Explain them in brief. [6]

D) Write short note on multidatabase system. [3]

Q. 5 A) Write short notes on: [8]

i) SOAP.

ii) XML DTD.

B) Explain the structure of XML data with example. [8]

OR

Q. 6 A) Explain the following with respect to web architecture; [8]

i) Web server.

ii) Common gateway interface.

iii) Cookie.

iv) Uniform Resource Locator.

B) Which are different parsers for XML? Explain them in detail. [8]

SECTION II

Q. 7 A) What are different data cleaning methods? [8]

Answer:

Data cleaning
(or data cleansing) routines attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data. 

1. Missing values

Ignore the tuple

Fill in the missing value manually

Use a global constant to fill in the missing value

Use a measure of central tendency for the attribute (such as the mean or median) to fill in the missing value 

Use the attribute mean or median for all samples belonging to the same class as the given tuple

Use the most probable value to fill in the missing value

2. Noisy data

Binning

Regression

Outlier analysis

B) Explain architecture of data warehouse with a neat diagram. [6]

C) A data warehouse can be modeled by either a star schema or a snowflake schema. Briefly describe the similarities and the differences of the two models, and then analyze their advantages and disadvantages with regard to one another. Give your opinion of which might be more empirically useful and state the reasons behind your answer. [4]

OR

Q. 8 A) Explain indexing OLAP data with example [6]

B) Explain the following operation on the multidimensional data with example. [6]

i) Roll up and drill down. ii) Slicing & dicing

C) Explain three different data warehouse models. [6]

Answer:

• Enterprise warehouse

  • collects all of the information about subjects spanning the entire organization

• Data Mart

  • a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart
    • Independent vs. dependent (directly from warehouse) data mart

• Virtual warehouse

  • A set of views over operational databases
  • Only some of the possible summary views may be materialized

 

Q. 9 A) Consider following training set. [8]

Class Label	A	B	C
C1	S	Y	X
C1	B	Y	X
C1	B	R	X
C1	S	R	X
C2	S	B	X
C2	B	B	Z
C2	B	Y	Z
C2	B	B	X
C2	S	Y	Z

Construct decision tree based on above training set using ID3.

B) Explain K mean algorithm with example. Also state it weakness [8]

OR

Q. 10 A) A database has 5 transactions. Let 𝑚𝑖𝑛 𝑠𝑢𝑝 = 0.6 and 𝑚𝑖𝑛 𝑐𝑜𝑛𝑓 = 0.8. [8]

Customer	Date	Items bought
100	10/15	{I,P,A,D,B,C}
200	10/15	{D,A,E,F}
300	10/16	{C,D,B,E}
400	10/18	{B,A,C,K,D}
500	10/19	{A,G,T,C}

i) List the frequent 𝑘-itemset for the largest 𝑘,

ii) List all the strong association rules (with support and confidence)

B) Explain in detail classification and prediction. What is the difference between them. [8]

Q. 11 A) What do you mean by relevance ranking? Explain TF/IDF methods of ranking. [8]

B) Explain the following: [8]

i) Inverted Index

ii) Ontology

iii) Stop Words.

iv) Random walk

OR

Q. 12 A) What is page ranking and popularity ranking? Explain in brief. [8]

B) Explain the following terms [8]

i) Web crawlers.

ii) Homonyms

iii) Vector space model

iv) Synonyms

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