Advanced Databases Pune University BE(IT) 2013 Question

Pune University BE(IT) 2013 Question Papers / Previous year BE(Information Technology) question papers of Pune University / BE IT Advanced Databases Question Paper / Pune University Questions with Answers - Regulation 2008

[Click on the links to view answers]

UNIVERSITY OF PUNE

[4364]-794

B. E. (IT) Examination - 2013

ADVANCED DATABASE MANAGEMENT

(JUNE 2008 Course)

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

[Total No. of Questions : 12]                                     [Total No. of Printed Pages :3]

Instructions :

(1) Answers to the section should be written in separate books

(2) Neat diagrams must be drawn wherever necessary.

(3) Assume suitable data, if necessary.

(4) Section I :Q1or Q2, Q3 or Q4, Q5 or Q6

(5) Section II: Q7 or Q8, Q9 or Q10, Q11 or Q12

SECTION-I

Q1. a) Explain parallel query evaluation using data partitioning technique (06)

Q1. b) Describe Partitionedparallel join. (06)

Q1. c) Describe the benefits and drawbacks of Partitioned parallelism. (05)

OR

Q2. a) Explain parallelizing individual operations using bulk loading and scanning, sorting. (06)

Q2.b) Explain parallel evaluation of a relational query in a DBMS with shared nothing architecture. (06)

Q2.c) Write a short Note of parallel Query optimization (05)

Q3. a) Explain 2PC and 3PC commit protocols (06)

Q3. b) Explain distributed catalog management in distributed databases. (06)

Q3. c) Write a short note on LDAP (05)

OR

Q4. a) Explain any two availability approaches in distribute databases (06)

Q4. b) Suppose that the employee relation is stored in Madison and the tuples with salary < 100000 are replicated at New York. Consider the following three options for lock management : all locks managed at a single site, say ABC; primary copy with Madison being the primary for employees; and fully distributed. For each of the lock management options, explain what locks are set for all following queries. Also state from which site the page is read

i. A query at Austin wants to read a page of employees tuples with salary <= 50000

ii. A query at Madison wants to read a page of employees tuples with salary <= 50000

iii. A query at New York wants to read a page of employees tuples with salary <= 50000 (06)

Q4. c) Write a short Note on Query Transformation (05)

Q5. a) Consider the following nested relational schema (10)

Emp= (ename, childrenset setoff(children), skillset setoff(skills)

Children = (name, birthday), Birthday= (day, month, year)

Skills = (type, Examset setoff(Exams)), Exams = (year, city)

Answer the following

i. Write DTD and XML file

ii. Write a query in XPath to list all skill types in Emp.

iii. Find the name of all the employees who have a child who has a birthday in March.

iv. Find the those employee who took an examination for the skill Type “typing” in the city “Pune”

v. List all skill types in Emp.

Q5. b) Compare and contrast the two-tier and three-tier architecture for Web-DBMS. Describe design issues for a web- DBMS based E-learning application which includes text, audio and video data. (06)

OR

Q6. a) Describe the various issues for efficient evaluation of XML Queries (06)

Q6. b) Write a short Note on (10)

1. XML facets

2. Web services and SOAP

SECTION II

Q7. a) Design basic conceptual schemas of conceptual modeling of data warehouse for Inventory application. (06)

Q7. b) Explain Load manager in data warehouse. (06)

Q7. c) Explain guidelines for designing fact tables (05)

OR

Q8. a) Explain Indexing techniques in data warehouse (06)

Q8. b) Explain Kimball’s nine steps design methodology (06)

Q8. c) Write a short notes on OLAP (05)

Q9. a) Describe Incremental mining algorithm for computing Frequent Itemsets with suitable example. (06)

Q9. b) Explain the splitting criteria and pruning process for decision tree construction with suitable example (06)

Q9. c) Write a short Note on Web Mining (05)

OR

Q10.a) Explain different types of clustering in data mining with suitable example (06)

Q10.b) Design a classification model using Naïve Bayesian classifier with suitable example (06)

Q10.c) Write a short note on Text Mining (05)

Q11. a) Explain the following terms in Information Retrieval with suitable example

i) Synonyms ii) Homonyms iii) Proximity iv) TF-IDF (06)

Q11. b) Write short notes on

i) Bit-sliced Signature File

ii) Precision and Recall  (10)

Q12. a) Define Information Retrieval System. Describe Web search engine architecture (06)

Q12. b) Write short notes on iii) Document Ranking iv) Link based searching approach (10)

*******************

Pune University BE/B.Tech Question Papers

Pune University BE CSE-IT Question Papers / Previous year BE-B.Tech question papers of Pune University / BE Advanced Databases Question Papers / BE Computer Engg. Advanced Databases Question Papers / BE Information Technology Advanced Databases Question Papers / Pune University Question Papers with Answers

• Advanced Databases (Regulation/Pattern 2008) - Year 2013
• Advanced Databases (Regulation/Pattern 2003) - Year 2013
• Advanced Databases (BE- IT) (Regulation/Pattern 2008) - Year 2013

• Advanced Database Management (Regulation/Pattern 2008) - Year Nov-Dec 2014

Pipelined Parallelism and Independent Parallelism

Pipelined Parallelism and Independent Parallelism / Types of Interoperation Parallelism / What is Pipelined Parallelism? / What is Independent Parallelism?

Interoperation Parallelism

It is about executing different operations of a query in parallel. A single query may involve multiple operations at once. We may exploit parallelism to achieve better performance of such queries. Consider the example query given below;

SELECT AVG(Salary) FROM Employee GROUP BY Dept_Id;

It involves two operations. First one is an Aggregation and the second is grouping. For executing this query,

We need to group all the employee records based on the attribute Dept_Id first.

Then, for every group we can apply the AVG aggregate function to get the final result.

We can use Interoperation parallelism concept to parallelize these two operations.

[Note: Intra-operation is about executing single operation of a query using multiple processors in parallel]

The following are the variants using which we would achieve Interoperation Parallelism;

1. Pipelined Parallelism

2. Independent Parallelism

1. Pipelined Parallelism

In Pipelined Parallelism, the idea is to consume the result produced by one operation by the next operation in the pipeline. For example, consider the following operation;

r1 ⋈ r2 ⋈ r3 ⋈ r4

The above expression shows a natural join operation. This actually joins four tables. This operation can be pipelined as follows;

Perform temp1 ← r1 ⋈ r2 at processor P1 and send the result temp1 to processor P2 to perform temp2 ← temp1 ⋈ r3 and send the result temp2 to processor P3 to perform result ← temp2 ⋈ r4. The advantage is, we do not need to store the intermediate results, and instead the result produced at one processor can be consumed directly by the other. Hence, we would start receiving tuples well before P1 completes the join assigned to it.

Disadvantages:

1. Pipelined parallelism is not the good choice, if degree of parallelism is high.

2. Useful with small number of processors.

3. Not all operations can be pipelined. For example, consider the query given in the first section. Here, you need to group at least one department employees. Then only the output can be given for aggregate operation at the next processor.

4. Cannot expect full speedup.

2. Independent Parallelism:

Operations that are not depending on each other can be executed in parallel at different processors. This is called as Independent Parallelism.

For example, in the expression r1 ⋈ r2 ⋈ r3 ⋈ r4, the portion r1 ⋈ r2 can be done in one processor, and r3 ⋈ r4 can be performed in the other processor. Both results can be pipelined into the third processor to get the final result.

Disadvantages:

Does not work well in case of high degree of parallelism.

Advanced Database Question Pune University - 2013

Pune University BE(CSE) Question Papers / Previous year BE(Computer Engg.) question papers of Pune University / BE CSE Advanced Databases Question Paper / Pune University Questions with Answers - Regulation 2003

[Click on the links to view answers]

[Total No. of Question=12]                                    [Total no. of printed pages= 3]

UNIVERSITY OF PUNE

[4364-296]

B.E.(Computer Engineering) Examination 2013

Advanced Databases

(2003 pattern)

Time-Three hours                                                             Maximum Marks-100

Instructions:

(1)Answer to the TWO sections should be written in separate answer books

(2)Neat diagrams must be drawn whenever necessary.

(3)Figures to the right indicate full marks.

(4)Assume suitable data whenever necessary.

SECTION-I

Q.1 (a) Describe and differentiate the following partitioning techniques. (8)

(i) RangePartitioning (ii) Round Robin

(b) Explain the following with suitable example. (8)

(i) Pipelinedparallelism (ii) Independent parallelism

OR

Q.2 (a) Explain in detail intraoperation and interoperating parallelism. (8)

(b) Describe how following operations are parallelized. (6)

(i) Selection (ii) Projection (iii) Aggregation

(c) What is meant by skew in Parallel databases? (2)

Q.3 (a) Explain in detail Deadlock Handling and availability with respect to distributeddatabases. (10)

(b) Explain distributed data storage. (8)

OR

Q.4 (a) What are different locking protocols in distributed databases ? Explain. (10)

(b) Explain data fragmentation in distributed databases. (8)

Q.5 (a) Explain the following terms. (6)

(i) SOAP (ii) X -query (iii) XML DTD

(b) Explain in detail different tiers in 3-tier architecture. (10)

OR

Q.6 (a) Explain the following terms. (6)

(i) Thin client (ii) Xpath (iii) Application server

(b) Explain in detail XML document. (10)

SECTION-II

Q.7 (a) Differentiate between OLTP and OLAP systems. (6)

(b) Explain in detail snowflake schema for the multidimensional databases. (10)

OR

Q.8 (a) Why data pre-processing is required? Explain phases of Data Pre-processing.

(10)

(b) Explain the following terms. (6)

(i) Data cube (ii) Slice and dice.

Q.9 (a) State and explain K-means Algorithm. (10)

(b) Write short note on. (8)

(i) Decision tree (ii) Outlier analysis

OR

Q.10 (a) State and Explain Apriori Algorithm. (10)

(b) Write short notes on. (8)

(i) Bayesian classifier (ii) Text Mining

Q.11 (a) Describe web crawler and web search engine in detail. (8)

(b) Explain the following terms. (8)

(i) Inverse document frequency

(ii) Homonyms

(iii) Synonyms

(iv) Term frequency

OR

Q.12 (a) Explain in detail popularity ranking. (8)

(b) Explain the following terms. (8)

(i) Inverted Index

(ii) Precision and recall

(iii) Stop words

(iv) Ontologies

************************