Kostogryzov-for china-2013

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

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  • 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 Ill not go into some details. They can be found in authors 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 (i(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 (t) = (min (1, 2) t)=1- (min (1, 2) > t)= = 1-(1 > t)(2 > t)= 1 [1-1(t)] [1- 2(t)] (t)=(max (1, 2) t)=(1 t)(2 t)=1(t)2(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) 13. 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) 14. 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 15. 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 16. 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 17. 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