# Project #2 Online data stream

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BLG 335E Analysis of Algorithms I
Project #2
Problem Definition
In an online data stream, statistics of the data hold the key that can provide some intuition about the
general trends and even have some predictions regarding the future. In this regard, the estimations of the
statistics on the large data pools should be as swift and precise as possible.
In this homework, we are aiming to find out the mean, standard deviation, and 5-figure summary of a
household electric power consumption1 data stream for each of the requests that have been made in an online
manner. In particular, a 5-figure summary consists of a minimum, 1st quartile, median, 3rd quartile, and
maximum element. To make it happen, it is crucial to store and process the incoming data in a time-efficient
manner.
To calculate the statistics of the online data stream efficiently, we ask you to choose appropriate data
structures and implement their methods. Afterwards, we would like you to adopt these data structures for
this problem.
Constraints
• The maximum number of samples is denoted by N, where 1 ≤ N ≤ 1000000.
• At least two samples have been provided before estimating any of the mean, standard deviation, or
5-figure summary statistics.
• WARNING: Your program runtime is subject to a timeout, you should be writing it as efficient as
possible. Try to remember the data structures as array, binary (search) tree, and heap to improve the
Sample Cases
In the first line of the test case, we provide the total number of statistic estimators, and we list their
names down below. As it can be seen in input1.txt, there are 7 estimators in total which are mean (for
average estimation), std (for standard deviation estimation), min (for minimum element estimation), firstq
(for the first quantile estimation), median (for median element estimation), thirdq (for the third quantile
estimation) and max (for maximum element estimation). After that, we leave one empty line, and state how
many lines are present for the rest of the test case and which feature of the dataset that we are interested
in, separated by a comma. After that, it is possible to proceed either with one of the following. First, we
can append a sample by writing ”add” (as in line 11) and then, the features of the data sample in the line
below (as in line 12). Here, we enter the day, time, global active power (gap), global reactive power (grp),
voltage (v) and global intensity (gi) features which are separated with commas. Second, it is possible to
flush the statistics by writing ”print”. When the print option is called, we should see the starting date,
starting time, latest retrieved date, latest retrieved time, mean, standard deviation, minimum value, first
quadrant, median, third quadrant, and maximum value in output1.txt, given that we have 7 estimators
shown in input1.txt.
1https://www.kaggle.com/datasets/thedevastator/240000-household-electricity-consumption-records/versions/1?
Page 1 of 4
BLG 335E Analysis of Algorithms I Homework #2
Code Listing 1: input1.txt
1 7
2 mean
3 std
4 min
5 firstq
6 median
7 thirdq
8 max
9
10 19 , gap
12 1/1/07 ,0:00:00 ,2.58 ,0.136 ,241.97 ,10.6
14 1/1/07 ,0:01:00 ,2.552 ,0.1 ,241.75 ,10.4
15 print
17 1/1/07 ,0:02:00 ,2.55 ,0.1 ,241.64 ,10.4
18 print
20 1/1/07 ,0:03:00 ,2.55 ,0.1 ,241.71 ,10.4
21 print
23 1/1/07 ,0:04:00 ,2.554 ,0.1 ,241.98 ,10.4
25 1/1/07 ,0:05:00 ,2.55 ,0.1 ,241.83 ,10.4
26 print
28 1/1/07 ,0:06:00 ,2.534 ,0.096 ,241.07 ,10.4
29 print
Code Listing 2: output1.txt
1 1/1/07 ,0:00:00 ,1/1/07 ,0:01:00 ,2.566 ,0.01979899 ,2.552 ,2.559 ,2.566 ,2.573 ,2.580
2 1/1/07 ,0:00:00 ,1/1/07 ,0:02:00 ,2.560667 ,0.01677299 ,2.550 ,2.551 ,2.552 ,2.566 ,2.580
3 1/1/07 ,0:00:00 ,1/1/07 ,0:03:00 ,2.5572 ,0.01285302 ,2.550 ,2.550 ,2.551 ,2.559 ,2.580
4 1/1/07 ,0:00:00 ,1/1/07 ,0:05:00 ,2.556 ,0.01186592 ,2.550 ,2.550 ,2.551 ,2.554 ,2.580
5 1/1/07 ,0:00:00 ,1/1/07 ,0:06:00 ,2.552857 ,0.01365563 ,2.534 ,2.550 ,2.550 ,2.553 ,2.580
Page 2 of 4
BLG 335E Analysis of Algorithms I Homework #2
Project Deliveries
• Your efficient C++ Implementation of online statistics estimator on household power consumption
data [70 pts]
• Report on the project [30 pts]
– BONUS: 1.333x multiplier on the report, if written with LaTeX. Please consider looking at this
repository if you are seeking a template: https://github.com/ongun-kanat/itu-report-templates
Report Structure
Your report should explain briefly the data structures that you have used in your implementation, where
you should also include the functions with their pseudocodes and asymptotic upper bounds. After that, in a
separate section, you should describe how exactly you used these data structures in this problem to estimate
the statistics. Similarly, you should include the functions for estimating the statistics with their pseudocodes
and calculated complexity values.
Then, create a line plot by recording how long it takes to complete the data structure operations and
how many times your implementation calls the data structure methods for all input text files and each of
the statistics estimators. This part should be repeated 10 times. Evaluate your results briefly to support
your claims on the calculated complexity values.
In a separate section, please provide an answer to the following question in your own words: Describe
whether it is possible to apply a sliding window-based approach to estimate the statistics while retaining the
same time complexity.
Page 3 of 4
BLG 335E Analysis of Algorithms I Homework #2
Submission Rules
• You should write your code in C++ language and try to follow an object-oriented methodology
with well-chosen variables, methods, and class names and comments where necessary.
• You cannot use the C++ Standard Template Library (STL) algorithms.
• You can define multiple classes in a single cpp file or use multiple cpp files with header files.
• It is mandatory to include a MakeFile which makes your code compiled with the ”make all” command.
• Also, make sure that your code can be run on our Docker container in the form of ./homework2
[input file].
• If the code is not self-explanatory and does not include adequate comments, a point penalty of up to
20 points will be applied.
• Hand-written reports will not be accepted. Also for pseudocodes, please prepare them properly, do not
copy and paste your exact C++ code that you used for your assignments.
• Do not share any code or text that can be submitted as a part of an assignment (discussing ideas is
okay).

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