Birla Institute of
Technology & Science, Pilani
Work Integrated
Learning Programmes Division
Second Semester 2021-2022
Comprehensive Test
(EC-3 Regular)
Course No. :
DSECLZC556
Course Title :
Stream Processing and Analytics
Nature of Exam :
Open Book
|
No. of Pages = 4 No. of Questions = 4
|
Duration :
2 Hours
Date of Exam : 06/03/2021 or 19/03/2021 (FN/AN)
Note to Students:
1. Please follow all the Instructions
to Candidates given on the cover page of the answer book.
2. All parts of a question
should be answered consecutively. Each answer should start from a fresh
page.
3. Assumptions made if any,
should be stated clearly at the beginning of your answer.
Q1.
When you decide to implement your own Bloom filter, you need to understand the
main formulas relating important parameters impacting the design of Bloom
filter, so that you can optimally configure the Bloom filter. Consider the
following notation for the four parameters of the Bloom filter:
·
f = the
false positive rate
·
m = number
of bits in a Bloom filter
·
n = number
of elements to insert
·
k = number
of hash functions
The formula that
determines the false positive rate as a function of other three parameters is
as follows (Formula 1):
a)
For
each of the following pair of bits-per-element value and number of hash
functions, compute the value of “f”. Show
all the necessary calculations. [10]
·
Bits-per-element:
5,6,8,10
·
Number
of hash functions: 1 to 10
|
k m/n |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|
5 |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
|
6 |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
|
8 |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
|
10 |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
F? |
b)
Plot
the graph of “f” against Bits-per-element and number of hash functions. [2]
c)
What
is impact of change in Bits-per-element on the false positive rate? [1]
d)
Is
there any relevant relationship that exhibit between the number of hash
functions and false positive rate? [1]
e)
If
the optimal k for a particular bits-per-element is given by
following formula, then for Bits-per-element value of 7, what is optimal number
of hash functions are required? [1]
Q2. The weighted moving average
(WMA) is generalization of the standard moving average that uses different
weights for each of the elements in the window. This collection of weights is
known as “kernels”. Consider the following implementation of WAM algorithm.
Public class WMA {
Double []
kernel;
Double []
values;
Double
kernelSum = 0;
Int k = 0;
Long N = 0;
Public WMA
(double [] kernel) {
this.kernel
= kernel;
for
( double j : kernel) kernelSum += j;
values
= new double[kernel.length];
}
Public
double observe (double x) {
Values
[k++] = x;
If
( k == values.length ) k = 0;
N++;
If
( N < kernel.length) return Double.NaN;
Double
y = 0;
For
( int i = 0; i < kernel.length; i++)
Y
+= kernel[i] * values [ (k+i) % values.length];
return
y/ kernelSum;
}
}
Assume the kernel weights are given as 1, 2, 3, and 1.
a) Compute
the WMA for each of the following “x” values. Show all the necessary
calculations. [7]
x
= 1, 2, 3, 4, 5, 6, 7, 8
b) Discuss
the impact of this algorithm in off-line and online processing environments. [1]
Q3. Consider the
following stream of events coming from a truck. Periodically these events are
received and processed on the server side for doing some rum time analytics as
shown in the query below.
Event Stream
data
|
ID |
Event |
Processing Time |
Status |
Qty |
|
Time |
||||
|
T1 |
11.2 |
12 |
Moving |
2 |
|
T2 |
11.15 |
12 |
Moving |
3 |
|
T3 |
11.09 |
12 |
Moving |
1 |
|
T1 |
11.5 |
12 |
Moving |
2 |
|
T2 |
11.45 |
12 |
Static |
3 |
|
T3 |
11.39 |
12 |
Broken |
1 |
|
T4 |
11.19 |
12 |
Moving |
2 |
|
T1 |
12.2 |
1 |
Moving |
2 |
|
T2 |
12.15 |
1 |
Static |
3 |
|
T3 |
12.09 |
1 |
Broken |
1 |
|
T4 |
11.49 |
1 |
Moving |
2 |
|
T1 |
12.5 |
1 |
Broken |
2 |
|
T2 |
12.45 |
1 |
Moving |
3 |
|
T3 |
12.39 |
1 |
Static |
1 |
|
T4 |
12.37 |
1 |
Moving |
2 |
The structured
streaming Query –
inputDataFrame.groupBy(Status).window(120
minutes).count(Qty)
Showcase the content of following
tables when the query is executed at 12.00 and 1.00 PM respectively.
[7]
a) Input
table
b) Result
table
c) Output
when mode is respectively
i.
Complete
ii.
Update
iii.
Append
Q.4. Look at following stream of
data values with time stamp. [1 + 2 + 1 + 1 + 3 + 2 = 10]
|
Value |
34 |
67 |
-6 |
78 |
34 |
12 |
90 |
45 |
12 |
|
time |
0 |
0 |
1 |
1 |
2 |
2 |
2 |
3 |
3 |
(For a, b, c) If a count based tumbling window is
defined with eviction policy set to 4,
a)
How many windows will be processed for
above stream of data values?
b)
What will be the difference between the
first and last data value in the second window?
c)
If the trigger policy is set to 2, then
how many times the code will be executed for query?
(For d, e) If a time based tumbling window is defined,
d)
With eviction policy set to 3 seconds, how
many windows will be processed for the above stream of data values?
e)
If the trigger policy and eviction policy
both are set to 1 second, what will be the average values (for each window)?
f)
If a sliding window is defined, with slide
interval 1 second and window length 1 seconds, what are the different windows
that will be visible for the given streaming data values?
***********
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