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COMP90056 Stream Computing and Applications
Assignment 2 (15 marks) — Frequent Items in a Data Stream
Posted date: Monday 5 October 2020
Due date: 17:00 Monday 26 October 2020
For this assignment you will investigate the performance of counter-based algorithms that estimate the most
frequent items of a data stream. These are known as StickySampling, LossyCounting and SpaceSaving,
described in  and . Similar to Assignment 1, you will summarise your findings and provide recommendations
in a report.
Task 1: Implementation
There is no specified programming language for this assignment. You may use whichever programming language
Implement each of the data sources, algorithms and performance metrics below.
• A stream of integers as input for the algorithms with frequencies that obey a Zipf (power-law) distribution.
Here the ith most frequent item has probability proportional to 1/iz, where z is a positive real-valued
parameter. Note that the most frequent items could appear anywhere in the stream, not only at or near
• The StickySampling algorithm, according to Section 4.1 of . This requires three input parameters:
support s (proportion of items that defines an item as frequent), error tolerance , and probability of
• The LossyCounting algorithm, according to Section 4.2 of . This requires two input parameters:
support s, and error tolerance .
• The SpaceSaving algorithm, according to Fig. 1 of  (or lecture notes). This requires a number of
counters t as input parameter (t = 1/s).
• Assume = s/10 in your implementations that depend on .
• You may use a hash map or equivalent structure to store the tracked items.
• Define the precision of an estimate to be the proportion of tracked items that are actually frequent (have
frequency greater than sN where N is the stream size).
• Define the relative error Erel of a frequency estimate fˆ compared to its true value f as |fˆ − f |/f .
• Compute the speed at which the frequent items problem is solved for each algorithm.
• Compute the number of items of the stream that are tracked at any point in the StickySampling and
Task 2: Report
Complete a short report analysing the three algorithms including the following points. For any randomised
algorithms, steps will need to be taken to make results more definitive. Plots should be well described and clear
to visualise. Refer to Section 4 of  for a good example of analysis including plots to describe the performance
of algorithms (your report is not expected to be as detailed). Avoid solely answering the points listed as if you
were making entries in an experimental logbook. Instead, it should read as a technical report with appropriate
• Set-up—A description of the experimental set-up to aid reproducibility. For example, include the CPU
frequency, memory, operating system, programming language, compiler (if applicable). [1 mark]
• Generate input data according to the power-law distribution and plot the distribution of frequencies of
items for z = 1.1, 1.4, 1.7, 2.0 with a logarithmic plot to demonstrate that they are as desired. In these
cases, how many items have frequency at least 1% of the total number of items? [2 marks]
Table 1: Theoretical Performance of the Algorithms
StickySampling LossyCounting SpaceSaving
• Describe in your own words (one paragraph) the intuition behind how the StickySampling algorithm
identifies frequent items. Cite any references used apart from . [1 mark]
• Complete Table 1 with theoretical values (in big-O notation) as a function of algorithm parameters.
Provide some justification of these formulas (they may depend on your implementation) and point out
any assumptions made. This will be used as a reference to compare with results you obtain in the practical
implementation. [1 mark]
• Evaluate the impact of different values of z (listed above) on the performance of the three algorithms. Fix
s = 0.001 and choose an appropriately large stream length. Plot precision vs skew and runtime vs skew,
commenting on your results. [2 marks]
• Evaluate the influence of support on memory and runtime for each of the three algorithms. Plot the
maximum number of tracked items vs support and also the runtime vs support (minimum s at least
10−4). Using these, obtain a third plot of the maximum number of tracked items vs runtime for all three
algorithms. Point out where the algorithms differ and possible reasons for this. [3 marks]
• Plot average relative error vs support for an appropriate choice of parameters for the three algorithms
commenting on your results. [2 marks]
• Practical considerations—point out any differences between practice and theory when running the algo-
rithms, and possible explanations for the difference. [1 mark]
• Conclusion and recommendation—which would be your recommended algorithm for practical use? Indi-
cate how your answer depends on the application (e.g., speed, accuracy or memory requirements). What
additional experiments would you suggest running to gain further insight? [2 marks]
The marks for each component in the report are as shown above. The grading will be based on the clarity and
rigour of your description or analysis for each part. The code will not be assessed, but should be presented and
documented well enough that others could reproduce the whole experimental study of your report and obtain
You should lodge your submission for Assignment 2 via the LMS (i.e., Canvas). You must identify yourself in
each of your source files and the report. Poor-quality scans of solutions written or printed on paper will not be
accepted. Solutions generated directly on a computer are of course acceptable. Submit two files:
• A report.pdf file comprising your report for the experimental study.
• A code.zip file containing all your source files of the implementations for the experiments.
Do not include the testing files, as these might be large. REPEAT: DO NOT INCLUDE TESTING FILES! It is
very important, so that you can justify ownership of your work, that you detail your contributions in comments
in your code, and in your report.
The late penalty for non-exam assessment is two marks per day (or part thereof) overdue. Requests for
extensions or adjustment must follow the University policy (the Melbourne School of Engineering “owns” this
subject), including the requirement for appropriate evidence. Late submissions should also be lodged via the
LMS, but, as a courtesy, please also email Chaitanya Rao when you submit late. If you make both on-time and
late submissions, please consult the subject instructor as soon as possible to determine which submission will
You are reminded that your submission for this Assignment is to be your own individual work. Students are ex-
pected to be familiar with and to observe the University’s Academic Integrity policy http://academicintegrity.
unimelb.edu.au/. For the purpose of ensuring academic integrity, every submission attempt by a student may
be inspected, regardless of the number of attempts made. Students who allow other students access to their
work run the risk of also being penalised, even if they themselves are sole authors of the submission in question.
By submitting your work electronically, you are declaring that this is your own work. Automated similarity
checking software may be used to compare submissions.
You may re-use code provided by the teaching staff, and you may refer to resources on the Web or in
published or similar sources. Indeed, you are encouraged to read beyond the standard teaching materials.
However, all such sources must be cited fully and, apart from code provided by the teaching staff, you must not
Despite all these stern words, we are here to help! Frequently asked questions about the Assignment will be
answered in the LMS discussion group.
 Manku, G. and Motwani, R. Approximate frequency counts over data streams. VLDB, pp. 346–357, 2002.
 Metwally, A., Agrawal, D., and El Abbadi, A. Efficient computation of frequent and top-k elements in data
streams. Database Theory, ICDT 2005, 10th International Conference, Edinburgh, UK, January 5–7, 2005,
pp. 398–412, 2005.
 Luo, G., Wang, L., Yi, K., and Cormode, G. Quantiles over data streams: experimental comparisons, new
analyses, and further improvements. The VLDB Journal vol. 25, pp. 449–472, 2016.