Knowledge Mining Based On Applications Of The Methods And Technologies Of Risks Prediction

1. ICTIS – 2013
SESSION 3A: TRANSPORTATION INFOMATION
PROCESSING THEORIES AND METHODS
Prof. Andrey Kostogryzov, Dr. Vladimir Krylov, Dr. Andrey Nistratov,
Dr. George Nistratov, Dr. Vladimir Popov
Moscow, Russia, www.mathmodels.net
Knowledge Mining Based On
Applications Of The Methods And
Technologies Of Risks Prediction

2. “One cannot embrace unembraceable”
Kozma Prutkov, Russia, 1883
INSTEAD OF INTRODUCTION
1. On the one hand we remember the doubts of the famous physicist Albert Einstein:
‘As far as the laws of mathematics refer to reality, they are not reliable;
and as far as they are reliable, they do not refer to reality’.
While understanding that this century-old dictum is negative
for the chances of ICTIS – 2013 success, all we should not
‘loose’ today advanced specialists in different areas (including physicists) !
2. On the other hand ISO/IEC has started activity to ‘embrace unembraceable’ by
international standards on system engineering (the first - ISO/IEC 15288).
Today it is not late to ‘embrace unembraceable’ on the base
of probability modelling yet (including the work of ICTIS-2013) !
Presented work is equally intended for those who are highly skilled in mathematics
and for those who are not very savvy in probability theory.
The goal is to propose original probability models, methods, and
software technologies of risks prediction for
Knowledge Mining and to ‘embrace unembraceable’ in practice
* To mitigate risk to lose the regulations of ICTIS I’ll not go into some details. They can be found in author’s publications

3. PART 1. GENERAL PROBLEMS
THAT ARE DUE TO BE AND CAN BE
SOLVED BY KNOWLEDGE MINING

4. Practical problems that are due to be solved by the mathematical modelling

5. The methods, models and software tools should be used in
system life cycle

6. What about the situation with Risks Prediction ?
The threats are inevitable, the requirements for risks predictions are objective!
The control reduces risks, but should be estimated
on integral level of efficiency
In different applications the used methods are specific, results are not comparable
Methods of risk prediction should be focused on Knowledge Mining to
define and use in time the effective preventive measures!

7. prove the probability levels of «acceptable quality and admissible
risk» for different systems in uniform interpretation,
create technics to solve different problems for quality and risk
optimization, provide access for wide use and training
The purposeful way to improve essentially the situation
From standard processes
of ISO/IEC 15288
consider
General
properties
of the
processes
developed
in time line
create universal
probability models and
software tools to predict,
analyze and optimize the
processes
approve the models
on practice examples
to do optimization
of quality and risks
It is important to support making-decisions by Knowledge Mining
and/or avoid wasted expenses in system life cycle
Expected pragmatic
effect from application

8. PART 2. EXAMPLE OF RISK PREDICTION
BY MODELLING PROTECTION
PROCESSES AGAINST DANGEROUS
INFLUENCES
(based on the theory of random processes)

9. Real used information
Interacted
systems
Subordinate
systems
SYSTEM
The general purpose of
operation:
to meet requirements for
providing reliable and timely
producing complete, valid
and confidential information
for its following use
Information system
Users
Purposes
Requirements to
information
system
Use
conditions
Operated
objects
Higher
systems
Resources
Sources
Example for prediction an
information quality
on probability level

10. 3. Automatic synthesis of more adequate distribution function (Вi(t))
during structure building by calculations from t=0 to ∞ with given
accuracy considering threats, control and monitoring for every element
SPECIFIC DIFFERENCES FOR INTEGRATED MODELS
1. Consideration of threats, control and monitoring and recovery
measures for complex system
2. Combination of different models,
including data mining as a result of
modelling and their use as input to
the next modelling
В(t) = Р(min (τ1, τ2) ≤ t)=1- Р(min (τ1, τ2) > t)=
= 1-Р(τ1 > t)Р(τ2 > t)= 1 – [1-В1(t)] [1- В2(t)]
В(t)=Р(max (τ1, τ2) ≤ t)=Р(τ1 ≤ t)Р(τ2 ≤ t)=В1(t)В2(t)
Example of “series” system
The cases 1, 4 illustrate dangerous influences

11. C O N T R O L O F Q U A L I T Y A N D R I S K S
P r o fi ts
a n d / o r
d a m a g e s
S T A T E M E N T O F P R O B L E M S
A n a ly s i s o f p u r p o s e s , f u n c t i o n a l
p o s s i b il it ie s a n d e n v ir o n m e n t
c o n d i t io n s o f s y s t e m o p e r a t i o n
A n a ly s i s o f s y s t e m o p e r a t i o n s c e n a r -
io s c o n s i d e r in g t h r e a t s
D e f in i t io n o f q u a l it y a n d r is k s
m e t r i c s in s y s t e m l if e c y c le
F o r m a l iz a t i o n o f p r o b l e m s
D e f i n i t i o n a n d s u b s t a n t i a t i o n
o f a c c e p t a b l e q u a l i t y a n d
a d m i s s i b l e r i s k s
E s t a b li s h m e n t o f r e a l r e q u ir e m e n t s
t o s y s t e m i n t e g r i t y
A N A L Y Z I S A N D O P T I M I S A T I O N
Im p r o v e d
a n d n e w
r e q u ir e m e n t s
a n d c o n d it i o n s
C o n d it i o n s ,
t h r e a t s
C o n d i t io n s ,
t h r e a t s ,
d a n g e r o u s
e v e n t s
a n d
i n f lu e n c e s
S y s t e m
d e s c r i p t i o n
S t u d i e d
p o s s i b i l i ti e s to
i m p r o v e q u a l i t y ,
m i t i g a t e r i s k s ,
d e c r e a s e
e x p e n s e s
J u s t i f i e d l e v e l s o f
a c c e p ta b l e q u a l i ty
a n d
a d m i s s i b l e r i s k s
S y s te m p r o j e c t .
O p e r a t i n g s y s t e m
M a n a g e d
p o s s i b i l i t i e s t o
i m p r o v e q u a l i t y ,
m i t i g a t e r i s k s ,
i n c r e a s e p r o f i t s
a n d / o r d e c r e a s e
e x p e n s e s a n d / o r
d a m a g e s
R e a l r e q u i r e m e n t s
t o s y s t e m i n t e g r i t y
E s t a b li s h m e n t o f t h e f o r m a l le v e l
o f a c c e p t a b l e q u a li t y
a n d a d m i s s ib l e r is k s
M a t h e m a t ic a l
m o d e l s , m e t h o d s
a n d s u p p o r t i n g
t h e m s o f t w a r e
t o o l s
S o lu t i o n o f t h e p r o b l e m s o f a n a ly si s
a n d s y n t h e s is
A n a ly s i s o f f u n c t i o n a l
p o s si b il it ie s a n d e n v ir o n m e n t
c o n d i t io n s o f s y s t e m o p e r a t i o n
A n a ly s i s o f s y s t e m o p e r a t i o n
s c e n a r i o s c o n s id e r i n g t h r e a t s ,
d a n g e r o u s e v e n t s a n d i n f lu e n c e s
R a t i o n a l
s t r a t e g y o f
q u a l it y
m a n a g e m e n t
in s y s t e m
li f e c y c l e
e t c .
Use of Knowledge Mining in quality and risk optimisation

12. PART 3. EXAMPLES
(Monitoring data and statistics can be used in real time of system
operation to predict risks and receive the mined knowledge about
the future critical time and the effectiveness of preventive actions.
An “Admissible risk” can be substantiated by “precedent
principle”)

14. Анализ рисков в опасном производстве
Input: a frequency of critical situations is 3 events per year, the mean time of situation evolution
before damaging is 1 hour. The railroad tracks integrity is confirmed on the central control station
once in a day while the dispatcher shifts are changed. Duration of integrity control is 1 hour on
average, the mean time between mistakes for the shift of monitoring to be 1 week or more.
Example 1. Estimation of control and monitoring for railroad
tracks. What about the risk for a time period of 1 year
To decrease risks the mean time between mistakes for the dispatcher
personnel should be increased, the time of carrying out control and
repairing damages should be shorten to several days or even hours
Risk during 1 month (columns 1, 4), 1 year (columns 2, 5), 10
years (columns 3, 6); integrity control and recovery time 1 hour
(columns 1-3) and 10 days (columns 4-6)
Dependency of the risk for 1 year as input data varying in the range of -50% +100% (variant 5: period of integrity control and recovery =10days)

15. Estimation 2. Knowledge Mining for complex multipurpose system
Integrated risk to lose integrity of
system during operational 1– 4
years grows from 0.11 to 0.67.
And the role of monitoring and
control is discovered
the “bottle-necks” are clear -
Nonmonotonic effects are the real arguments to find the measures and timeline
moments for optimizing processes, elements, subsystems and system operation

16. 2005
2008 2010
2007
Innovative management of quality and risks
in systems life cycle
Standardization, mathematical modeling,
rational management and certification in the
field of system and software engineering
System foundations of the management of
competitiveness in oil and gas complex

17. The offered methodology helps to answer many system
questions, for example:
«How to meet rationally the requirements of the international standards?», understanding
as it a high degree of quality, safety and competitiveness;
«Whether may be the set requirements met from system point of view?», it means that for
the developer it is important to be convinced, whether it is capable and what for this purpose is
requested;
«Whether are expected effects achievable?», it means for the customer and the developer it is
especially important to understand, on what all the same they can really count after end of the
project within the limits of the allocated resources;
«How much safe are those or other scenarious?», including security from terrorist threats or
natural cataclysms;
«What measures should be more effective?» etc.
The methodology is used in practice to predict quality and
risks as applied to newly developed and currently
operated manufacture, power generation, transport,
engineering, information, control and measurement,
quality assurance, and security systems

18. Rational use of the methodology allows to go
«from a data mining according to events to knowledge
mining from transportation monitoring data and statistics»
Conclusion