Karolina Alexiou discusses the importance of evaluation in big data analysis, emphasizing the need for a robust framework to measure the effectiveness of algorithms. She shares insights from Teralytics' case study on traffic congestion estimation, highlighting the challenges of obtaining accurate ground truth data and the pitfalls of comparison methods. Key action points include asking critical questions about data verification and visualization, and leveraging appropriate tools like Python and pandas for effective analysis.

1. Closing the Loop
Evaluating Big Data Analysis
Karolina Alexiou

2. About
The speaker
● ETH graduate
● Joined Teralytics in September 2013
● Data Scientist/Software Engineer
The talk (takeaways)
● Point out how evaluation can improve your project
● Suggest concrete steps to build an evaluation
framework

3. The value of evaluation
Data analysis can be fun and exploratory, BUT:
“If you torture the data long enough,
it will confess to anything.”
-Ronald Coase, economist

4. The value of evaluation
Without feedback on the data analysis results, (=closing
the loop) I don’t know whether my fancy algorithm is better
than a naive one.
How to measure?

5. Strategy
People-driven
● Get a 2nd opinion on your methodology
Data-driven
● Get another data source to verify results (ground truth)
● Convert ground truth and your output to the same
format
● Compare against meaningful metric
● Store & visualize results

6. General evaluation framework

7. General evaluation framework
Statistical significance?

8. Teralytics Case Study: Congestion
Estimation
Ongoing project: Use of cellular data to
estimate traffic/congestion in Swiss roads
Our estimations: Mean speed on a highway at
a given time, given location

9. Ground truth
● Complex algorithm with lots of knobs and subproblems
● How to know we’re changing things for the better?
● Collect ground truth regarding road traffic in Switzerland
-> sensor data available from 3rd party site
● Write hackish script to login to website and fetch sensor
data that match our highway locations
● Instant sense of purpose :)

10. Same format
Not just a data architecture problem.
● Our algorithm’s speed estimations are fancy averages
of distance/time_needed_for_distance (journey speed)
● Sensor data reports instantaneous speed.
● Sensors are probably going to report higher speeds
systematically (bias).

11. Comparing against metric
● Group data every 3 minutes
● Metric: Percentage of data where the
difference between ground truth and
estimation is <7%
● Other options
○ linear correlation of time-series of speed
○ cross-correlation to find optimal time shift

12. Pitfalls of comparison
● Overfitting to ground truth
● Correlation may be statistically insignificant
Need proper methodology (training set/testing
set) & adequate amounts of ground truth

13. Visualization
● Instant feedback on
what is working and
what is not.
● Insights
○ on assumptions
○ on quality of data sources
○ presence of time shift

14. Lessons learned
Ground truth isn’t easy to get
● No API - web scraping
● May be biased
● May have to create it yourself

15. Lessons learned
Use the right tools
● The output of a Big Data analysis problem is of more manageable size ->
no need to overengineer, python is fitting for the job
● Need to be able to handle missing data/add constraints
/average/interpolate-> use existing library (pandas) with useful abstractions
● Crucial to be able to pinpoint what goes wrong -> interactivity (ipython),
logging

16. Lessons learned
Use the right workflow
● Run the whole thing at once for timely feedback
● Always visualize -> large CSVs are hard to make sense
of (false sense of security)
● Iterative development pays off & is sped up by
automated evaluation :)

17. Action Points
Ask questions
● Is there some place of my data analysis where my
results are unverified?
● Am I using the right tools to evaluate?
● Is overengineering getting in the way of quick & timely
feedback?

18. Action Points
Make a plan
● What ground truth can I get or create?
● How can I make sure I am comparing apples to apples?
● How should I compare my data to the ground truth
(metric, comparison method)?
● What’s the best visualization to show correlation?

19. Recommended Reading
● Excellent abstractions for data
cleaning & transformation
● Good performance
● Portable data formats
● Increases productivity
● +ipython for easy exploring of
the data (more insight, what
went wrong etc)
It takes some time to learn to use the
full power of pandas - so get your
data scientists to learn it asap. :)

20. Recommended Reading
● Even new companies have
“legacy” code (code that is
blocking change)
● Acknowledges the imperfection
of the real world (even if design
is good, problems may arise)
● Acknowledges the value of
quick feedback in dev
productivity
● Case-by-case scenarios to
unblock yourself and be able to
evaluate your code

21. Recommended Reading

22. Thanks
I would like to thank my colleagues for making
good decisions, in particular
● Valentin for introducing pandas to Teralytics
● Nima for organizing the collection of ground truth on
several projects
● Laurent for insisting on testing & best practices

23. Questions?
We are hiring :)
Looking for Machine Learning/Big Data experts
Experience with pandas is a plus
Just send your CV to recruiting@teralytics.net

24. Bonus Recommended Reading
Evaluation of impact of
charity organizations is a
hard, unsolved problem
involving data
● transparency
● more motivation to
give