IOT holds a special promise for testing-engineering, in 3 ways: (a) be able to get performance data in field conditions; (b) failure analysis; (b) the same sort of analytics can be used to get important feedback from early stage experimentation and trials. IOT data is unsupervised thus present a special challenge (the same as in big data, not fit for Statistics)

1. Unsupervised data
the big promise for
Testing
Edith Ohri
Tel. 054-3179161
edith@datalert.co.il

2. Testing stands for -
Ensuring that a product is functioning well and
in compliance with relevant standards,
and that it can sustain rough conditions and
handling.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 2

3. The problem in testing new
products
New product functions don’t have yet standards,
the testing therefore becomes more
complicated expensive and lengthy.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 3
Checking a new helmet in the old days…

4. Big Data new promise
14Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 4

5. Unsupervised data solution
Free data from early testing and external
repositories can help in establishing new
standards and functionality.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 5

6. Pros and Cons
Unsupervised data are indeed in abundance, and
rich in information, which enable shorter and
more productive R&D, but such data are not
representative, they include redundant variables,
unknown interrelations, text variables,
inconsistencies and errors, which make them unfit
to Statistics, UNLESS ..
(Unless one can observe patterns of behavior in
that data and create new perfectly reasonable
hypotheses – which is what GT data mining does)14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 6

7. Using GT patterns of behavior
GT data mining breaks down the data to
similarity-homogeneous groups that enable:
• Focus on particular groups
• Identifying exceptions
• Drilldown to root causes
• Discovery
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 7

8. Example: testing a new
polygraph
The polygraph has 8 channels, 5 are new.
The testing is required to validate that the new
device identifies liars effectively.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 8

9. Method
Focus & Drilldown
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 9
Groups map – liars are marked with red circle

10. GT findings: lying patterns
and indicators
1. High tension throughout the interview – (skin
perspiration) ASR is found to be significant if
higher than 1.05
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 10
ASR as a function of question no.
0
0.5
1
1.5
2
2.5
0 20 40 60 80 100 120 140
‫שאלה‬ '‫מס‬
ASR
Poly. (‫אמת‬ ‫)דוברי‬
Poly. (‫שקרנים‬)

11. More GT patterns and indicators
2. Larger difference of blood-pressure between
arm and head in the first half of interrogation.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 11

12. More GT patterns and indicators
3. Faster reaction in “tough” questions
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 12

13. More GT patterns and indicators
4. Typical camouflage strategies:
a. A restrained reaction before lying followed
by a “relief” signal afterward.
b. Irrelevant answers.
c. Inconsistent reactions.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 13

14. Benefits of early test by GT
• Confirmation of product performances
• Gauging new standard’s parameters
• Finding hidden patterns
• Early feedback to designers & engineers
• Stronger case for investors, clients and mgm.
• As results, improving the design, reducing risk,
and time to market, and increasing chances
for success.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 14

15. About
Edith Ohri, Engr. from the Technion (Israel) and MSc
from NY polytech. Main field: data analytics algorithms.
Developer of the "GT data mining" solution. GT as
software started in 2002, Singapore. It has been applied
in diverse fields and proved effective also for Big Data.
Founder of Datalert (startup) and head of Quality
branch in the Industrial & Management Engr.
Association.
Current ly: Predictive Analytics for health monitoring by
IOT, Finances, and Mining Big Data new standard.
14Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri 15

16. Thanks
Don’t be afraid to try new things.
Remember, Noah Arc was built by amateurs.
Professionals built the Titanic.
(- Source unknown)
Edith Ohri
14Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri 16