ICTIS – 2013SESSION 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

“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

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

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

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

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!

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 processesof ISO/IEC 15288

consider

Generalproperties

of the processesdeveloped in time line

create universalprobability 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

PART 2. EXAMPLE OF RISK PREDICTIONBY MODELLING PROTECTION

PROCESSES AGAINST DANGEROUSINFLUENCES

(based on the theory of random processes)

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

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

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 i l i t i e 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 i o n o f q u a l i t y a n d r i s k s m e t r i c s in s y s t e m l i f e c y c l e

F o r m a l i z 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 l i 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

I m 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 t i 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 t a 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 l i s h m e n t o f t h e f o r m a l l e v e l 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 ib l e r i s k s

M a t h e m a t i c 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 l u t i o n o f t h e p r o b l e m s o f a n a ly s i s a n d s y n t h e s i s

A n a ly s i s o f f u n c t i o n a l p o s s i b i l i t i e 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 i t y

m a n a g e m e n t in s y s t e m l i f e c y c l e

e t c .

Use of Knowledge Mining in quality and risk optimisation

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”)

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)

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

2005

2008 2010

2007

Innovative management of quality and risksin 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

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

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