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Big Data Analytics Question Paper - May 17 - Electronics And Telecomm (Semester 7) - Mumbai University (MU)

## Big Data Analytics - May 17

### Electronics And Telecomm (Semester 7)

Total marks: 80

Total time: 3 Hours
INSTRUCTIONS

(1) Question 1 is compulsory.

(2) Attempt any **three** from the remaining questions.

(3) Draw neat diagrams wherever necessary.

**Answer the following:**

**1.a.**Explain different Distance measures for Big Data.

**1.b**Explain the Hadoop Architecture with its features.

**1.c**Explain CAP Theorem? how it is different from ACID Properties.

**1.d**What are the shortcomings of nearest neighbour technique in collaborative filtering method? Suggest some improvements.

**2.a**Write a Map-Reduce Algorithm for Binary search tree. Explain the flow of execution.

**2.b**Suppose a stream consists of the integers 2,1,6,1,5,9,2,3,5. Let the hash functions all be of the from h(x)=ax+b mod 16 for some a & b. You should treat the results as a 4 bit binary integer. Determine the tail length for each stream element and the resulting estimate of the number of distinct elements if the hash function is:

a) h(x) = 2x+3 mod 16

b) h(x) = 4x+1 mod 16

c) 5x mod 16

**3.a**Explain Different types of recommendation system with real time examples.

**3.b**Consider the portion of a Web graph as shown in Figure-1 i) Compute the hub and authorities scores for all nodes.

ii) Does this graph contain spider traps? Dead ends? If so, which nodes.

**4.a.i.**Write a short note on PCY Algorithm

**4.a.ii.**Write a short note on CURE algorithm

**4.b**Imagine there are 100 baskets, numbered 1,2,....,100 items, similarity numbered. Item 1 is in basket J if and only if I divides J evenly. For example basket is 24 is the set of items {1,2,3,4,6,8,12,24}. Describe all the association rules that have 100% confidence.

**5.a**Define Bloom Filter. Explain the concept of Bloom filter. Algorithm with example.

**5.b**Explain HITS algorithm with example.

**6 Answer any two of the following**

**6.a**NoSQL architectural pattern with example.

**6.b**Matrix Multiplication by Map Reduce

**6.c**List & explain Big data:- 1) Characteristics 2) Types 3) Challenges

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