Ideal reference model. Adaptive regulation according to the reference model

Number

Name

Level 1

Running process

AP 1.1

Process execution attribute

Level 2

Managed Process

AP 2.1

Execution control attribute

AP 2.2

Work Product Management Attribute

Level 3

Established process

AP 3.1

Process definition and transformation attribute

AP 3.2

Process resource attribute

Level 4

predictable process

AP 4.1

Process dimension attribute

AP 4.2

Process control attribute

Level 5

Optimizing Process

AP 5.1

Process change (verification) attribute

AP 5.2

Improvement Opportunity Attribute

Scale

Process attributes

Grade

Level 1

Process execution

Mainly or completely

Level 2

Process execution

Execution control

Work Product Management

Fully

Mainly or completely

Mainly or completely

Level 3

Process execution

Execution control

Work Product Management

Process resource

Fully

Fully

Fully

Mainly or completely

Mainly or completely

Level 4

Process execution

Execution control

Work Product Management

Process definition and transformation

Process resource

Process measurement

Process control

Fully

Fully

Fully

Fully

Fully

Mainly or completely

Mainly or completely

Level 5

Process execution

Execution control

Work Product Management

Process definition and transformation

Process resource

Process measurement

Process control

Process change

Possibility of further improvement

Fully

Fully

Fully

Fully

Fully

Fully

Fully

Mainly or completely

Mainly or completely

Process

Number

Name

Number

Name

Acquisition (basic)

Acquisition preparation (component)

Vendor selection (component)

Vendor Check (Component)

Customer approval (component)

Support (Basic)

Requirements setting (new)

Operation (advanced)

Functional Usage (Advanced Component)

User Support (Advanced Component)

Development (basic)

Analysis and development of system requirements (component)

Software requirements analysis (component)

Software development (component)

Software design (component)

Software integration (component)

Software testing (component)

System testing and integration (component)

Operation of the system and software (basic)

Supporting life cycle processes

Documentation (advanced)

Configuration management (basic)

Quality assurance (basic)

Verification (basic)

Validation (Basic)

Joint review (baseline)

Check (basic)

Problem solving (basic)

Measurement (new)

Reusable (new)

Management (basic)

Project management (component)

Quality management (new)

Risk management (new)

Organizational alignment (new)

Improvement process (basic)

Create a process (component)

Process evaluation (component)

Process improvement (component)

Human Resource Management (advanced)

Infrastructure (basic)

Table 4.4.

Activities and processes 12207

Processes 15504

Initial life cycle processes

Acquisition process

Acquisition process

basic

Initialization

Acquisition preparation process

Component

Preparing a Bid-for-Proposal [-bid]

Supplier selection process

Component

Contract preparation and adjustment

Supplier selection process

Component

Vendor verification

Vendor Verification Process

component

Acceptance and completion

Customer approval process

component

Delivery process

Delivery process

basic

Initialization

Delivery process

basic

Preparing a response

Delivery process

basic

Contract

Delivery process

basic

Planning

Delivery process

basic

Execution and management

Delivery process

basic

Review and evaluation

Delivery process

basic

Delivery and completion

Delivery process

basic

Requirements setting process

Development process

Development process

basic

Process Implementation

Development process

basic

Analysis of system requirements

component

System architecture development

System requirements development and analysis process

component

Analysis of software requirements

Software requirements analysis process

component

Software architecture development

Software development process

component

Working draft software

Software development process

component

Software coding and testing

Software Design Process

component

Software integration

Software integration process

component

Software qualification testing

Software testing process

component

System integration

component

System Qualification Testing

System testing and integration process

component

Software installation

Delivery process

basic

Software support

Delivery process

basic

Functioning process

basic

Process Implementation

Functional use process

extended component

Functional testing

Functional use process

extended component

System operation

Functional use process

extended component

User support

User support process

extended component

Operating process

basic

Process Implementation

Software and system operation process

basic

Analysis of problems and modifications

Software and system operation process

basic

Implementation of the modification

Software and system operation process

basic

Commissioning

Software and system operation process

basic

Migration

Software and system operation process

basic

Software recycling

Software and system operation process

basic

Supporting life cycle processes

Documentation process

Documentation process

extended

Process Implementation

Documentation process

extended

Design and development

Documentation process

extended

Products

Documentation process

extended

Exploitation

Documentation process

extended

Configuration management process

Basic

Process Implementation

Configuration management process

basic

Configuration Identification

Configuration management process

basic

Configuration control

Configuration management process

basic

Accounting for configuration status

Configuration management process

basic

Configuration evaluation

Configuration management process

basic

Release and delivery management

Configuration management process

basic

Quality Assurance Process

Quality Assurance Process

basic

Process Implementation

Quality Assurance Process

basic

Product Warranty

Quality Assurance Process

basic

Process Guarantee

Quality Assurance Process

basic

Quality assurance systems

Quality Assurance Process

basic

Verification process

Verification process

basic

Process Implementation

Verification process

basic

Verification

Verification process

basic

Validation Process

basic

Process Implementation

Validation Process

basic

Validation

Validation Process

basic

Joint review process

Joint review process

basic

Process Implementation

Joint review process

basic

Project Management Reviews

Joint review process

basic

Technical reviews

Joint review process

basic

Verification Process

Verification Process

basic

Process Implementation

Verification Process

basic

Verification Process

basic

Problem Solving Process

Problem Solving Process

basic

Process Implementation

Problem Solving Process

basic

Solution of problems

Problem Solving Process

basic

Measurement process

Process reuse

Organizational life cycle processes

Management Process

Management Process

basic

Initialization and Scoping

Project Management Process

component

Planning

Project Management Process

component

Execution and control

Project Management Process

component

Review and evaluation

Project Management Process

component

closure

Project Management Process

component

Quality Management Process

Risk management process

Organizational alignment process

Infrastructure Process

Infrastructure Process

basic

Process Implementation

Infrastructure Process

basic

Creation of infrastructure

Infrastructure Process

basic

Infrastructure operation

Infrastructure Process

basic

Improvement process

Improvement process

basic

Create a process

Process creation process

component

Process evaluation

Process evaluation process

component

Process improvement

Improvement process

component

Process preparation

extended

Process Implementation

Human resource management process

extended

Substantial development preparation

Human resource management process

extended

Preparing the implementation of the plan

Human resource management process

Annotation: The basics of the process approach to IT management are outlined, which is based on the concept of the value chain by M. Porter. The task is to organize effective IT management based on best practices. The role of standards in the field of IT is discussed.

The meaning of the value chain is to distinguish between the main and auxiliary groups of the organization's business processes. The main process groups add value to the product or service produced by the business, the auxiliary ones do not. As can be seen from fig. 1.1, the "IT Management" process group refers to the auxiliary ones, along with such process groups as "Human Resources Management" or "Financial Management" (of course, not all auxiliary groups are shown in the figure).

Of course, there are also such activities where IT management is an essential part of the business and can reasonably be attributed to the main groups of processes. These are, for example, businesses where Internet services play an important role: retail banks, online stores or trading platforms in the Internet. Fundamentally important Information Technology for telecom operators, or, say, global navigation service providers, not to mention companies operating in the IT sector. In such companies, some IT management processes (for example, providing customers with access to information resources of the company) become part of the main production process of the company, and those groups of IT management processes that are used, for example, when performing internal automation projects or when interacting with corporate users, will be auxiliary. information systems with a technical support group.

In addition to cases where a single IT organization is responsible for IT management in a company, there are also situations where IT management decentralized. This tends to happen in large geographically dispersed companies, although there are situations where several IT organizations coexist in the same company. Process approach to IT management has the advantage that it allows you to ignore the differences in the structures and organizational forms in which IT management activities take place, focusing on the main thing - the results and effectiveness of this activity. From a practical point of view, this means that "end-to-end" IT management processes must be defined and organized, involving employees of several business units involved in IT management. As experience shows, this is a difficult task, which is not always possible to solve. Organizational boundaries often turn out to be real barriers to the flow of information in processes. To overcome this difficulty, the concept of a process owner is introduced. Process owner takes full responsibility for the effectiveness, efficiency and improvement of the process. Determining the role and place of the owners of IT management processes in the organizational structure of the company is one of the difficult management tasks that have to be solved when implementing processes.

If with the help of the common value chain shown in Fig. 1.1, build a chain for an IT organization, you get the picture shown in fig. 1.2. Here, the supporting process groups, shown in white, are common to the IT organization with the corresponding processes of the business as a whole, and the rest of the supporting process groups are specific to IT management. It is they, along with the main groups of processes, that are of interest to us.

The main groups of processes in fig. 1.2 demonstrate the modern view of the activities of the IT organization, proposed in recent years. It consists in the fact that this activity is considered as the provision of services to business.

I will not specify yet what is meant by a service. At an intuitive level of understanding, the service of an IT organization is the provision of an information resource for solving a business problem. An information resource can be, for example, a software system or an application, data network or a human resource in the form of an IT specialist. Only one thing is important: this view of IT governance implies that there is a finite and relatively stable list of services agreed with the business, and the IT organization is solely responsible for their implementation.

If you consistently adhere to this view, then the composition of the main and auxiliary groups of processes of the IT organization becomes clear.

The main groups of processes are related to the planning, creation, implementation, maintenance and development of services. The processes in these groups include close interaction with customers and users who work in the company's core business.

Auxiliary process groups are associated with the creation, support and development of information resources (not all such groups are shown in Fig. 1.2). Examples are groups of IT infrastructure management processes, processes for creating and maintaining applications (they are commonly called management processes). life cycle information systems), training processes for specialists, as well as groups of processes that manage the use of temporarily attracted information resources belonging to subcontractors, suppliers and outsourcers.

It is worth highlighting a group of strategic planning processes that determine the long-term policy for the development of services and related information resources. This policy should be linked to the corporate business strategy and business goals. As a result of the implementation of the relevant processes, plans for the development of services of an IT organization are formed that meet business development plans, and plans for the development of information resources arising from them.

The rest of this book will focus on activities to improve the efficiency of the IT organization, that is, to improve the effectiveness and efficiency of the main and supporting IT management processes. This activity, in turn, can be considered as a group of processes for managing a specific information resource- knowledge and processes of the IT organization. On fig. 1.2 it is called "Improving IT Management Processes and Organizational Improvement".

So how can you improve the effectiveness and efficiency of core and support IT management processes?

Of course, there is no universal algorithm or ready-made recipe for all occasions. Nevertheless, it is possible to offer reasonable practical approaches to solving the problem, using knowledge, methods, techniques and tools developed and tested over the years of the existence of IT organizations around the world.

One of the widespread methods of improvement is the introduction of "best management practices" into the management practice of the enterprise. From the point of view of the process approach to IT management, reference process models, created as a result of generalization of the relevant world experience, should be considered as the best practices. Reference models exist primarily in the form of international standards developed by the International Organization for Standardization (ISO 3 International Organization for Standardization; for the origin of the ISO name, see http://ru.wikipedia.org/wiki/ISO) and other authoritative international and national organizations. It is important to understand that the standard process model is not an ideal role model applicable in all cases of life, but represents only an average experience that is recognized by the professional community and, therefore, may be useful in solving the problem of improving the efficiency of IT management in a particular organization.

Reference models that can be used to improve IT management processes are discussed in lectures 3-5 ("Process standards. GOST R ISO / IEC 12" Process standards. GOST R ISO / IEC 12207, "" Implementation of GOST R ISO / IEC 12207 "Implementation of GOST R ISO / IEC 12207", "Process Design. Standard IEEE 1074" Construction processes. Standard IEEE 1074", ""Development of the life cycle process model. GOST R ISO/IEC 15288" Development of the life cycle process model. GOST R ISO/IEC 15288").

Reference process models exist, in particular, for such groups of processes as information systems life cycle management, almost all groups of main processes, supplier and subcontractor management. At the same time, for groups of processes of strategic management or selection of a subcontractor, there are no well-developed reference models (more precisely, I am not aware of their existence).

To understand the reference models of processes and their relationships (sometimes quite complex and non-obvious), it is useful to study the so-called Body of Knowledge, or methodological reference books, where descriptions of tasks, concepts, objects and processes related to a particular area are collected and presented in a structured form. activities. In particular, the SWEBOK (Software Engineering Body of Knowledge) methodological guide is most relevant to IT management.

Finally, there is a fairly extensive corpus of methodological guides 4Eng. framework, which accumulate practical experience in solving management problems (COBIT, Val IT, Risk IT can serve as examples). They can be used in the implementation of processes such as strategic management, investment management and IT risk management.

More advanced methods for improving IT management processes are explored in Lectures 7-9 ("Maturity of Design Organizations. CMM Methodology" Maturity of Design Organizations. CMM Methodology", "Practical Use of CMM. SPICE Project" Practical use CMM. SPICE Project", "CMMI Conceptual Model" conceptual model CMMI").

The problem of improving the main processes of an IT organization that interacts with the business processes of an enterprise is considered in various aspects in lectures 10-13 ("IT Service Management Processes and the ITIL Library" IT Service Management Processes and the ITIL Library "," "ITIL Library . Service Delivery Strategy" ITIL Library . Strategy provision of services", " "ITIL Library. Service Design" ITIL Library . Service Design", "ITIL Library. Service Deployment, Delivery and Continuous Improvement" ITIL Library . Deployment, Delivery and Continuous Improvement of Services.

So far, we have only talked about the processes of an IT organization within a single company. In fact, an IT organization typically interacts with a number of external counterparties. An example of the relationship of an IT organization with external market actors is shown in fig. 1.3. The participation of external counterparties in the processes of an IT organization generates a structure called, by analogy with the value chain, the value added network 5Eng. value network. An example in fig. 1.3 shows only one layer of the network: IT organization - business partner. In practice, there can be any number of these levels; each counterparty has its own counterparties, etc.

The value-added network demonstrates the importance of business relationship management processes for an IT organization. For this reason, many process reference models include supplier and subcontractor management processes. It is worth noting that approaches to improving IT management processes used by an IT organization can be and are used by independent IT market entities in the same way to improve the efficiency of their processes. At the same time, the processes of managing the life cycle of IS, which are auxiliary for an IT organization, are among the main ones for a company - a software developer or supplier. The service delivery processes of an IT organization are of the same nature as the service delivery processes used by external providers. Auxiliary to the IT organization, the IT strategic planning process groups are the main ones for consulting companies providing professional services in the field of IT management. Even within the same company, there may be subtle issues associated with the dual role of processes. An example is when different instances of process 6 A process instance is an implementation of a process in a specific environment, for example, in a certain project or when solving a specific problem. used for different purposes and in different contexts. For example, different instances of a single project management process can be created when managing external and internal projects of a company. For the purposes of this discussion, the differences in the role of the process are not significant, so processes are not divided into primary and secondary processes in the rest of the book and are simply referred to as IT management processes.

An example of applying the process approach when describing the interaction of an IT organization with outsourcers is considered in the final lecture ("Outsourcing IT Management Processes" Outsourcing IT Management Processes "). It is necessary to immediately make a reservation that outsourcing in general and IT outsourcing in particular, it is a very complex and multifaceted concept, not yet fully understood by modern management theory. I am considering a rather special case of IT outsourcing to demonstrate practical benefit that can bring process approach when organizing outsourcing interaction.

As discussed above, one recognized source of IT management process reference models is information technology standards. Despite the fact that their total volume is quite large, there are several, in my opinion, fundamental standards that IT managers need to get acquainted with. It is about them that will be discussed further. I hope that by starting with these standards, the reader will be able to delve further into the subject on his own as much as he needs to.

I did not set myself the goal of a complete presentation of the standards (as well as other methods discussed below), so some of the ideas and methods given there are inevitably missing here (and the choice of standards itself is inevitably subjective). It was more important to show that there are deep, although not always obvious, links between different reference models, processes and methodologies developed at different times and for different purposes. They show how the foundations of a future theory emerge from separate attempts to comprehend and formalize a specific IT management experience.

Brief summary

The lecture suggests using process approach to the analysis of the activities of IT organizations and IT companies. The value chain and network of the IT organization are presented. The task was set to improve IT management processes. The role of process standards representing reference models of processes is considered.

Questions

1. What is a value chain? What does a sample value chain look like for an IT organization?
2. What is the difference between main and auxiliary processes? Can a process be both primary and secondary?
3. What is a value added network?
4. What is the role of IT standards in IT governance?

Methodological materials on organizational, methodological, psychological and pedagogical support for professional growth, self-realization of teachers and the formation of key competencies, the profile of competencies of a pedagogical worker were developed by the regional scientific and methodological center for expert assessment of pedagogical activity of the State Budgetary Educational Institution of Higher Education of the Moscow Region "Academy of Social Management"

The text is provided for review.
Developed in the regional scientific and methodological center for expert assessment of pedagogical activity, the reference model of the competencies of a pedagogical worker, due to its characteristics, is a normative, prognostic model aimed at results, therefore it underlies the control and measuring materials used in certification, determining their goals, objectives and content .

We present the reference model of the competencies of a pedagogical worker in a graphical and descriptive form.

Picture 1- Reference model of key competencies of a teacher

Reference model of competencies of a pedagogical worker(Figure 1) is an ideal, verbalized, i.e. encoded by signs of natural language, model of a teacher, which is an ideal image, a standard of a specialist that meets all the requirements for teaching staff during certification for the first and highest qualification categories of paragraphs. 30, 31 of the Procedure for attestation of teaching staff of state and municipal educational institutions, the requirements set out in the unified qualification directory for the positions of managers, specialists and employees (annex to the order of the Ministry of Health and Social Development of the Russian Federation of August 26, 2010 No. 761 n), and professional standards.

When designing a reference model of the competencies of a pedagogical worker, we relied on the author's developments, various scientific schools, in particular, we used domestic research by I.A. Zimney, N.V. Kuzmina, A.K. Markova, and foreign studies of the Council of Europe.

Key competence we consider it as an integral characteristic of a pedagogical worker, allowing him to freely navigate in the social and professional space, perform professional activities efficiently and effectively, solve standard and non-standard professional and pedagogical tasks, be a socially adapted person capable of constant personal and professional self-development.

The scope of competence is competency profiles as components of its knowledge, skills and attitudes that meaningfully determine competence.

Figure 2- Special and professional competence

Special and professional competence (Picture 2), i.e. possession of the actual professional activity at a sufficiently high level, the ability to design their further professional development.

understanding the purpose, mission of the profession;

possession of the norms of professional activity, high efficiency;

achievement of high results and their stability; professional skill;

professional consciousness (realization of the maximum number of signs of professional activity: content, means, results of labor);

professional thinking, professional intuition, independence in solving professional problems;

the optimal psychological price of the result, the absence of fatigue and overload.

As part of special and professional competence the following competency profiles :

1. Subject Competence , i.e. depth, consistency of knowledge on the subject and their application in pedagogical practice; the ability to implement the curriculum of basic and elective courses in various educational organizations.

2. Organizational and methodological competence , i.e. willingness to apply modern educational methods and technologies, including information, to ensure the quality of the educational process; activities, actions, techniques, skills, methods of work, techniques used in this profession to successfully achieve results; the ability to organize the educational activities of students (pupils).

3. Diagnostic Competence , i.e. possession of psychological and pedagogical knowledge, psychological and pedagogical actions, methods, techniques, skills, techniques, technologies; the ability to apply modern methods of diagnosing the achievements of students and pupils; provide pedagogical support for the processes of socialization and professional self-determination of students, preparing them for a conscious choice of profession.

4. Analytical and evaluation competencies , i.e., the ability to analyze and evaluate the formation of universal educational activities, mental operations of students, taking into account their individual characteristics and capabilities, both in qualitative and quantitative indicators (points in the rating, categories, etc.); apply methods of mathematical and statistical processing of information; participate in professional tests, the result of which is a differentiated (qualitative and quantitative) assessment of professionalism.

5. Predictive competence , i.e., the ability to determine the prospects for growth, the zone of proximal development of their students and their professional development; be aware of the potential of students and their own; awareness of development prospects and opportunities for their implementation (prognostic criteria); self-design, self-experimentation; building your own professional growth strategy, building and implementing the scenario of your professional life; consistency between the motivational and operational side of the activity.

6. Research competence , i.e., the ability to apply the methods of theoretical and experimental research; plan, organize, conduct and analyze a pedagogical experiment on the introduction of innovations; ability to analyze and synthesize; research skills; the ability to generate new ideas (creativity); demonstrate an understanding of the quality of research relevant to the discipline; demonstrate an understanding of experimental testing of scientific theories.

Figure 3 -

Communicative competence(Figure 3) - the competence of social interaction as the ability to establish mutual understanding adequately to situations, avoid conflicts, create a climate of trust; attributing oneself to a professional community; possession of the norms of professional communication, ethical standards of the profession; orientation of professional results for the benefit of other people, their spiritual enrichment by means of their profession; ability to cooperate, make contacts, easy compatibility; competitiveness, the ability to arouse interest in the society in the results of one's professional activity.

Communicative competence appears in the followingcompetency profiles :

1. Social and communicative competence , i.e., the ability to find verbal and non-verbal means and ways of forming and formulating thoughts in the course of its generation and perception, adequate to situations of interaction; the ability to use public speech skills, including in the field of broadcasting one's own experience (the ability to broadcast one's own positive experience to the pedagogical community: articles, speeches, participation in competitions; the ability to conduct discussions, polemics; readiness to interact with colleagues).

2. Organizational and communicative competence , i.e. the ability to organize productive communication and cooperation of schoolchildren; the ability to conduct educational classes in the form of dialogues, polemics, disputes, discussions, exchange of opinions, scientific disputes, etc.

Figure 4- Information competence

Information Competence(Figure 4) is related to information technology proficiency:

• receiving, processing, issuing information; transformation of information (reading, note-taking);
• mass media, multimedia technologies, computer literacy;
• possession of electronic, Internet technology.

Information competence is manifested in the following profiles:

1. Information retrieval competence , i.e., the ability to find the necessary information from various sources.

2. Information and analytical competence , i.e. skills to analyze information and manage it; willingness to use the main methods, ways and means of obtaining, storing, processing information; willingness to work with a computer as a means of information management; ability to work with information in global computer networks.

3. Information Technology Competence , i.e. the ability to use, reproduce, improve the means and methods of obtaining and reproducing information in print and in electronic format; knowledge of basic application programs and the ability to use them; Computer skills.

Figure 5- Personal competence

Personal Competence, i.e., stable professional motivation, the presence of a positive self-concept, a creative attitude, conscious professional creativity, changing oneself by means of a profession; individuality in professional work; openness to continuous professional training, experience accumulation, change; possession of methods of self-realization and development of individuality within the framework of the profession, readiness for professional growth, the ability for individual self-preservation; self-development of professional abilities; strong goal setting; professional training; reliance on the past professional experience, succession; increasing individualization and relative autonomy as one grows professionally.

Profiles personal competence:

1. Competence of self-development and self-expression - stable motivation, ability to set goals, professional abilities, professional learning, self-presentation, positive emotions; ability and readiness for education throughout life, possession of methods of personal self-expression and self-development, means of confronting professional deformations of the personality.

2. Reflective competence - a system-forming component of professional pedagogical activity and the quality of a person, which allows for the most effective and adequate reflection, which ensures development and self-development, contributes to creativity in educational and professional activities, achieving their maximum efficiency and effectiveness; acmeological phenomenon, contributing to the achievement of the highest results in activities; professional and personal qualities of the teacher, his readiness and ability for reflective activity using knowledge, skills, professional and life experience; ability to introspection and self-assessment.

reference model

reference model(English) reference model, master model) is an abstract representation of concepts and relationships between them in some problem area. On the basis of the reference model, more specific and detailed models are built, eventually embodied in real-life objects and mechanisms. The concept of a reference model is used in computer science.

Examples of Reference Models

• OSI network model (Open Systems Interconnection Reference Model),
• model of the Open Geospatial Consortium (English),
• von Neumann architecture - reference model model with sequential computations,
• State Enterprise Architecture Reference Model (eng.),
• Reference Information Model HL7 (Reference Information Model, RIM HL7),
• Reference Model (RM) openEHR .

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See what the "Reference Model" is in other dictionaries:

reference model- hierarchical model - [L.G. Sumenko. English Russian Dictionary of Information Technologies. M .: GP TsNIIS, 2003.] Topics information technology in general Synonyms hierarchical model EN reference model ...

reference model- etaloninis modelis statusas T sritis automatika atitikmenys: angl. mastermodel; reference model vok. Referenzmodell, n rus. reference model, f pranc. modele de référence, m; model standard, m … Automatikos terminų žodynas

reference model- 3.1.41 reference model given object in general, simplifying the breakdown of links by topic, which can be ... ... Dictionary-reference book of terms of normative and technical documentation

OSI reference model- The Open Systems Interaction Model developed by ISO in 1984. Allows you to universally describe the logic of information exchange between interconnected systems and subscribers. The complete model contains seven levels. At the bottom... ... Technical Translator's Handbook

ISO/OSI reference model- Seven-layer reference model of data transfer protocols. Defines the layers: physical, link, network, transport, session, presentation, and application. In CAN networks, only physical, channel, and application layers … Technical Translator's Handbook

broadband ISDN protocol reference model- The model includes four horizontal layers (Physical, ATM, ATM adaptations and upper levels) and three vertical planes (user, control and administration). Correspondence between models In ISDN and OSI is provided on the physical ... ... Technical Translator's Handbook

BOC reference model- EMWOS A model developed by the ISO, containing seven levels (layers) of protocols and designed for communication between devices in a network. [E.S. Alekseev, A.A. Myachev. English Russian explanatory dictionary of computer systems engineering. Moscow 1993] Topics ... ... Technical Translator's Handbook

reference model for open systems interaction- - Telecommunication topics, basic concepts EN ISO / OSI reference model ... Technical Translator's Handbook

protocol reference model- - [L.G. Sumenko. English Russian Dictionary of Information Technologies. M .: GP TsNIIS, 2003.] Topics information technology in general EN protocol reference modulePRM ... Technical Translator's Handbook

open systems interconnection reference model- - [L.G. Sumenko. English Russian Dictionary of Information Technologies. M .: GP TsNIIS, 2003.] Topics information technology in general EN reference model of open systems ... Technical Translator's Handbook

Books

• Computer networks. In 2 volumes. Volume 1. Data transmission systems, R. L. Smelyansky. The theoretical foundations of data transmission systems, the characteristics of the main types of physical media, the methods of coding and transmission of analog and digital data, the basics of organizing ...

Model classification

The problem of classifying models, like any fairly complex phenomena and processes, is complex and multifaceted. The objective reason for this is that the researcher is only interested in one property (or several properties) of the system (object, process, phenomenon), for which the model was created to display. Therefore, the classification can be based on many different classification features: description method, functional purpose, degree of detail, structural properties, scope, etc.

Consider some of the most commonly used classes (types) of models (Table 1.4.1).

Table 1.4.1

Material(physical, real) models - models built by means of the material world to reflect its objects, processes.

Ideal(imaginary) models - models built by means of thinking on the basis of our consciousness.

Informational(abstract, theoretical) models - models built on one of the languages ​​(sign systems) for encoding information.

material models are real, material constructions that serve to replace the original in a certain respect. The main requirement for the construction of this class of models is the requirement of similarity (similarity, analogy) between the model and the original. There are several types of similarity - geometric, physical, analogy, etc.

geometric similarity is the main requirement for the construction of geometric models, which are an object that is geometrically similar to its prototype and serves for demonstration purposes. Two geometric figures are similar if the ratio of all corresponding lengths and angles is the same. If the similarity coefficient is known - the scale, then by simply multiplying the dimensions of one figure by the scale value, the dimensions of the other figure are determined. AT general case such a model demonstrates the principle of operation, the mutual arrangement of parts, the process of assembly and disassembly, the layout of the object and is intended to study properties that are invariant (independent) of the absolute values ​​of the linear dimensions of the object. Examples of geometric models are: car models, mannequins, sculptures, prostheses, globes, etc. They depict the prototype not in all the variety of its properties, not in any qualitative boundaries, but within purely spatial ones. Here there is a similarity (similarity) not in general between things, but between special types of things - bodies. This is the limitation of this class of models. Note that a direct similarity is realized here.

physical likeness refers to the model and the original of the same physical nature and reflects their similarity in the similarity of the ratios of the physical variables of the same name at the corresponding spatio-temporal points. Two phenomena are physically similar if, according to the given characteristics of one, it is possible to obtain the characteristics of the other by simple recalculation, which is similar to the transition from one system of units of measurement to another. Geometric similarity is a special case of physical similarity. With physical similarity, the model and the original may be in more complex geometric relationships than linear proportionality, since the physical properties of the original are not proportional to its geometric dimensions. Here it is important that the space of physical variables of the model be similar to the space of physical variables of the original. In this case, the physical model in relation to the original is an analogy of the type of isomorphism (one-to-one correspondence). The central problem is the problem of correctly recalculating the results of a model experiment to the results of testing the original in real conditions. The similarity is based on the observance of certain physical criteria.

Ideal(imaginary) models are ideal constructions in our consciousness in the form of images or ideas about certain physical phenomena, processes, objects, systems (geometric point, infinity, etc.).

abstract(theoretical, informational) models - models representing modeling objects in a figurative or symbolic form.

Some hypothesis 1 about the properties of matter, assumptions about the behavior of a complex system under conditions of uncertainty, or a new theory about the structure of complex systems can serve as examples of abstract models.

On abstract models and on speculative analogy (similarity) between the model M and original S an abstract (theoretical) modeling is being built.

A striking representative of abstract and iconic modeling is a mathematical model.

Mathematical model– this is a set of mathematical formulas, equations, relationships, describing the properties of the modeling object that are of interest to the researcher.

Appropriate models can be used to study each aspect of modeling (type, structure, behavior) or their combination: appearance models, structure models, behavior patterns.

Appearance model most often comes down to enumeration of the external features of the modeling object and is intended for identification (recognition) of the object.

Structure model is a list of the constituent elements of the modeling object indicating the relationships between these elements and is intended for visual display, studying properties, identifying significant relationships, and studying the stability of the modeling object.

Behavior Model is a description of changes in the appearance and structure of the modeling object over time and as a result of interaction with other objects. The purpose of behavior models is to predict the future states of the modeling object, manage objects, establish links with other objects external to the modeling object.

Objectively, the levels of our ideas, the levels of our knowledge about various phenomena, processes, systems are different. This is reflected in the ways in which the phenomena under consideration are presented.

To informal Models include displays (images) obtained using various forms of thinking: emotions, intuition, imaginative thinking, subconsciousness, heuristics as a set of logical techniques and rules for finding truth. In non-formalized modeling, the model is not formulated, but instead some fuzzy mental reflection (image) of reality is used, which serves as the basis for making a decision.

An example of indefinite (intuitive) ideas about an object is a fuzzy description of a situation based on experience and intuition.

To formalized figurative models can be attributed to models, when models are built from any visual elements (elastic balls, fluid flows, trajectories of movement of bodies, etc.).

Formalizable abstract models include sign models, including mathematical constructions, programming languages, natural languages, along with the rules for their transformation and interpretation.

According to their purpose, the models are designed to solve many problems:

research(descriptor, cognitive, conceptual, formal) models are designed to generate knowledge by studying the properties of an object;

educational models are designed to transfer knowledge about the object under study;

workers(optimization, management) models are designed to generate the right actions in the process of achieving the goal.

To research models include semi-natural stands, physical models, mathematical models. Note that research models can act as training models if they are intended to transfer knowledge about the properties of an object. Examples of working models are: robot; autopilot; mathematical model of the object, built into the control or monitoring system; artificial heart, etc. At the same time, research and educational models should approach reality, and working models should reflect this reality. There is no clear boundary between these models. So, for example, a research model that adequately reflects the properties of an object can be used as a working one.

Research models are carriers of new knowledge, training models combine old knowledge with new ones.

Working models idealize the accumulated knowledge in the form of ideal actions to perform certain functions that it would be desirable to implement.

Descriptor Models- descriptive models, designed to establish the laws of change in the parameters of these processes and are implementations of descriptive and explanatory meaningful models at the formal level of modeling.

An example of such a model is a model of the motion of a material point under the action of applied forces, using Newton's second law. By setting the position and speed of the point at the initial moment of time (input values), the mass of the point (model parameter) and the law of change of applied forces (external influences), it is possible to determine the speed and coordinates of the point at any subsequent time moment (output values).

cognitive(mental, cognitive) models - models representing a certain mental image of the object, its ideal model in the head of the researcher, obtained as a result of observing the original object.

Forming such a model, the researcher, as a rule, seeks to answer specific questions, therefore, everything unnecessary is cut off from the infinitely complex structure of the object in order to obtain a more compact and concise description of it.

Cognitive models are subjective, as they are formed speculatively on the basis of all previous knowledge and experience of the researcher. One can get an idea of ​​a cognitive model only by describing it in a symbolic form. The representation of a cognitive model in natural language is called content model .

Cognitive and content models are not equivalent, because the former may contain elements that the researcher cannot or does not want to formulate.

conceptual model It is customary to call a meaningful model, the formulation of which uses the concepts and representations of subject areas of knowledge involved in the study of the object of modeling.

In a broader sense, a conceptual model is understood as a meaningful model based on a particular concept or point of view.

formal model is a representation of a conceptual model by one or more formal languages(for example, languages ​​of mathematical theories, universal modeling language or algorithmic languages).

AT humanities the modeling process in many cases ends with the creation of a conceptual model of the object.

In the natural sciences and technical disciplines, as a rule, it is possible to construct a formal model.

Thus, cognitive, content and formal models constitute three interrelated levels of modeling.

Optimization Models- models designed to determine the optimal (best) parameters of the modeled object from the point of view of some criterion or to search for the optimal (best) control mode for some process.

As a rule, such models are built using one or more descriptive models and include some criterion that allows you to compare different options for sets of output values ​​with each other in order to choose the best one. Restrictions in the form of equalities and inequalities associated with the features of the object or process under consideration can be imposed on the range of input parameters.

An example of an optimization model is the simulation of the process of launching a rocket from the Earth's surface in order to lift it to a given height in minimum time under restrictions on the magnitude of the engine impulse, the time of its operation, the initial and final mass of the rocket. Mathematical relations of the descriptive model of rocket motion act in this case in the form of constraints of the type of equalities.

Note that for most real processes, structures, it is required to determine the optimal parameters according to several criteria at once, i.e. we are dealing with so-called multiobjective optimization problems.

Management Models– models used to make effective managerial decisions in various areas of purposeful human activity.

In general, decision-making is a process comparable in complexity to the process of thinking in general. However, in practice, decision-making is usually understood as the choice of some alternatives from a given set of them, and the overall decision-making process is represented as a sequence of such choices of alternatives.

Unlike optimization models, where the selection criterion is considered to be certain and the desired solution is established from the conditions of its extremality, management models require the introduction of specific optimality criteria that allow one to compare alternatives under various uncertainties of the problem. The type of optimality criterion in managerial models is not fixed in advance. This is the main feature of these models.

Recording Models are models designed to register properties and qualities of interest to the researcher that are not available for direct registration on the modeling object.

When solving control problems for complex dynamic objects, reference and predictive models are used, which are a formalized display of the desired characteristics of the control object for the purposes of current or future control of the object.

reference model is a model that describes in one form or another the desired (idealized) properties of the modeling (control) object.

Predictive Models– models designed to determine future states ( future behavior) of the simulation object.

simulation models- this is a set of descriptions of the elements of the system, the relationship of elements with each other, external influences, algorithms for the functioning of the system (or rules for changing states) under the influence of external and internal disturbances.

Simulation models are created and used when the creation of a single model of a complex system is impossible or very difficult, the available mathematical methods do not allow obtaining satisfactory analytical or numerical solutions of the problems under consideration. But the presence of descriptions of elements and algorithms of functioning allows you to simulate the process of functioning of the system and produce measurements characteristics of interest.

It can also be noted that simulation models can be created for a much wider class of objects and processes than analytical and numerical models. In addition, since, as a rule, computing means (computers and other means) are used for implementation, universal or special algorithmic languages ​​serve as means of a formalized description of simulation models.

Simulation modeling in the study of large (complex) systems

remains practically the only available method for obtaining information about the behavior of the system under conditions of uncertainty, which is especially important at the stage of its design. Using this method, you can choose the structure, parameters and control algorithms of the synthesized system, evaluate their effectiveness, and also simulate the behavior of the system under conditions that cannot be reproduced on a real prototype (for example, accidents, failures, emergencies, etc.). When, in simulation modeling, the behavior of a system is studied under the action of random factors, followed by statistical processing of information, it is advisable to use the static modeling method as a method of machine implementation of the simulation model. In this case, the method of statistical tests (Monte Carlo method) is considered as a numerical method for solving analytical problems.

A special class of models are cybernetic models that reflect the management aspects of the behavior of complex systems based on information exchange between its elements. The very physical nature of cybernetic models differs from the physical nature of the prototype and its elements. A feature of cybernetic models is the possible presence in them, in addition to the control mechanism, of mechanisms of self-organization, learning, adaptation, etc., and in more complex systems, artificial intelligence.

Taking into account the time factor in modeling leads to the use of static and dynamic models.

Static Models reflect the steady (equilibrium) modes of operation of the system;

Static modes of operation of elements, objects, systems are reflected in their static characteristics (linear, non-linear) and are described by the corresponding algebraic functional dependencies.

Dynamic Models reflect unsteady (non-equilibrium, transient) modes of operation of the system.

To describe non-equilibrium (transient) modes of operation of the system, the most commonly used differential equations or systems of differential equations.

Let us consider some properties of models that allow, to one degree or another, either to distinguish or identify the model with the original (object, process). It is customary to single out the following properties of models: adequacy, complexity, finiteness, truth, proximity.

Adequacy. Under adequacy Models are usually understood as the correct qualitative and quantitative description of an object (process) according to a selected set of characteristics with a certain reasonable degree of accuracy.

Adequacy is the most important requirement for a model; it requires the model to correspond to its real object (process, system, etc.) with respect to the selected set of its properties and characteristics. This means adequacy not in general, but adequacy in terms of those properties of the model that are essential for the researcher. Full adequacy means the identity between the model and the prototype.

A mathematical model can be adequate with respect to one class of situations (the state of the system + the state of the environment) and not adequate with respect to another. The use of an inadequate model can lead either to a significant distortion of the real process or properties (characteristics) of the object under study, or to the study of non-existent phenomena, properties and characteristics.

You can introduce the concept of the degree of adequacy, which will vary from 0 (lack of adequacy) to 1 (full adequacy). The degree of adequacy characterizes the proportion of the truth of the model with respect to the selected characteristic (property) of the object under study. Note that in some simple situations, the numerical estimation of the degree of adequacy is not particularly difficult. The difficulty in assessing the degree of adequacy in the general case arises from the ambiguity and fuzziness of the adequacy criteria themselves, as well as from the difficulty of choosing those features, properties and characteristics by which adequacy is assessed.

The concept of adequacy is a rational concept, therefore, increasing its degree should also be carried out at a rational level. The adequacy of the model must be checked, controlled, refined constantly in the process of research on particular examples, analogies, experiments, etc. As a result of the adequacy check, it is found out what the assumptions made lead to: either to an acceptable loss of accuracy, or to a loss of quality. When checking the adequacy, it is also possible to justify the validity of the application of the accepted working hypotheses in solving the problem or problem under consideration.

Simplicity and complexity. Simultaneous requirement of simplicity and adequacy of the model is contradictory. From the point of view of adequacy, complex models are preferable to simple ones. In complex models, one can take into account more factors influencing the studied characteristics of objects. Although complex models more accurately reflect the simulated properties of the original, they are more cumbersome, hard to see and inconvenient to use. Therefore, the researcher seeks to simplify the model, since it is easier to operate with simple models. When striving to build a simple model, the basic model simplification principle:

the model can be simplified as long as the basic properties, characteristics and patterns inherent in the original are preserved.

This principle points to the limit of simplification.

At the same time, the concept of simplicity (or complexity) of a model is a relative concept. The model is considered fairly simple if modern facilities studies (mathematical, informational, physical) make it possible to conduct a qualitative and quantitative analysis with the required accuracy. And since the possibilities of research tools are constantly growing, those tasks that were previously considered difficult can now be classified as simple.

A more difficult task is to ensure the simplicity / complexity of the model of a complex system consisting of separate subsystems connected to each other in a certain hierarchical and multiply connected structure. At the same time, each subsystem and each level has its own local criteria of complexity and adequacy, which are different from the global criteria of the system.

In order to reduce the loss of adequacy, it is more expedient to simplify the models:

1) on physical level while maintaining the basic physical relationships,

2) at the structural level with the preservation of the main system properties.

Simplification of models at the mathematical level can lead to a significant loss of the degree of adequacy. For example, truncation of the high-order characteristic equation to the 2nd - 3rd order can lead to completely wrong conclusions about the dynamic properties of the system.

Note that simpler models are used to solve the synthesis problem, and more complex exact models are used to solve the analysis problem.

Finiteness of models. It is known that the world is infinite, like any object, not only in space and time, but also in its structure (structure), properties, relations with other objects. Infinity is manifested in the hierarchical structure of systems of different physical nature. However, when studying an object, the researcher is limited by the finite number of its properties, connections, resources used, etc. It is as if it “cuts out” some finite fragment from the infinite world in the form of a specific object, system, process, etc. and tries to cognize the infinite world through the final model of this fragment.

The finiteness of system models lies, firstly, in the fact that they reflect the original in a finite number of relations, i.e. with a finite number of connections with other objects, with a finite structure and a finite number of properties at a given level of study, research, description, available resources. Secondly, that the resources (information, financial, energy, time, technical, etc.) of modeling and our knowledge as intellectual resources are finite, and therefore objectively limit the possibilities of modeling and the very process of knowing the world through models. Therefore, the researcher (with rare exceptions) deals with finite-dimensional models.

The choice of model dimension (its degree of freedom, state variables) is closely related to the class of problems to be solved. An increase in the dimension of the model is associated with problems of complexity and adequacy. In this case, it is necessary to know what is the functional relationship between the degree of complexity and the dimension of the model. If this dependence is power-law, then the problem can be solved by using computer systems. If this dependence is exponential, then the “curse of dimensionality” (R. Kalman 1) is inevitable and it is almost impossible to get rid of it.

As noted above, an increase in the dimension of the model leads to an increase in the degree of adequacy and, at the same time, to the complication of the model. At the same time, the degree of complexity is limited by the possibility of operating with the model, i.e. the modeling tools available to the researcher. The need to move from a rough simple model to a more accurate one is realized by increasing the dimension of the model by involving new variables that are qualitatively different from the main ones and which were neglected when constructing a rough model. These variables can be assigned to one of the following three classes:

1) fast-flowing variables whose extent in time or space is so small that in a rough examination they were taken into account by their integral or averaged characteristics;

2) slow flowing variables whose extent of change is so great that in rough models they were considered constant;

3) small variables(small parameters), the values ​​and influences of which on the main characteristics of the system are so small that they were ignored in rough models.

Note that the division of the complex motion of the system in terms of velocity into fast and slow motions makes it possible to study them in a rough approximation independently of each other, which simplifies the solution of the original problem. As for small variables, they are usually neglected when solving the synthesis problem, but they try to take into account their influence on the properties of the system when solving the analysis problem.

When modeling, they try to identify, if possible, a small number of main factors, the influence of which is of the same order and is not too difficult to describe mathematically, and the influence of other factors can be taken into account using averaged, integral, or "frozen" characteristics.

Approximation of models. It follows from the above that the finiteness and simplicity (simplification) of the model characterize quality difference (at a structural level) between the original and the model. Then the approximation of the model will characterize quantitative side of this difference.

It is possible to introduce a quantitative measure of approximation by comparing, for example, a rough model with a more accurate reference (complete, ideal) model or with a real model. Approximation of the model to the original inevitable, exists objectively, since the model as another object reflects only individual properties of the original. Therefore, the degree of approximation (proximity, accuracy) of the model to the original is determined by the formulation of the problem, the purpose of modeling.

An excessive desire for increased accuracy of the model leads to its significant complication, and, consequently, to a decrease in its practical value. Therefore, apparently, the principle of L. Zadeh 1 is true that when modeling complex (man-machine, organizational) systems, accuracy and practical meaning are incompatible and exclude each other. The reason for the inconsistency and incompatibility of the requirements for accuracy and practicality of the model lies in the uncertainty and fuzziness of knowledge about the original itself - its behavior, its properties and characteristics, about the behavior of the environment, about the mechanisms for forming the goal, ways and means to achieve it, etc.

The truth of the models. Each model has a grain of truth, i.e. any model in some way correctly reflects the original. The degree of truth of the model is revealed only when practical comparison it with the original, because only

practice is the criterion of truth.

On the one hand, any model contains unconditionally true, i.e. definitely known and correct. On the other hand, the model also contains conditionally true, i.e. true only under certain conditions. A typical modeling error is that researchers use certain models without checking their truth conditions, the limits of their applicability. This approach obviously leads to incorrect results.

Note that any model also contains the supposedly true (plausible), i.e. something that can be either true or false under conditions of uncertainty. Only in practice is the actual relationship between true and false in specific conditions established. Thus, when analyzing the truth level of the model, it is necessary to find out:

1) accurate, reliable knowledge;

2) knowledge that is reliable under certain conditions;

3) knowledge estimated with some degree of uncertainty;

4) knowledge that cannot be assessed even with some degree of uncertainty;

5) ignorance, i.e. what is unknown.

Thus, assessing the truth of a model as a form of knowledge comes down to identifying the content in it of both objective, reliable knowledge that correctly reflects the original, and knowledge that approximately evaluates the original, as well as what constitutes ignorance.

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