using unsupervised data for testing in product development experiments conference ניסויים...
TRANSCRIPT
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
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…
Big Data new promise
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri Slide 4
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
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
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
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
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
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
מס' שאלה
AS
R
Poly. (דוברי אמת)
Poly. (שקרנים )
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
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
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
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
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.
14 Apr 2016 © Using unsupervised data for Testing with GT data mining. All rights reserved, Edith Ohri 15