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Presentation on the topic: Modeling information systems
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The purpose of the course is to deepen core subjects (computer science, mathematics); formation of competencies for professional activity in the area of information modeling Motivation of students when choosing EC. - testing students of their abilities and interest in creativity, research activities in the field of information modeling; - preparation for entering the university for specialties related to information modeling and computer technology: Applied Mathematics, modeling, computing systems etc.
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Contents of the textbook Chapter 1. Modeling of information systems 1.1. Information systems and systemology 1.2. Relational model and databases (Access) 1.3. Spreadsheet- information modeling tool 1.4. Application programming (VBA elements for Excel) Chapter 2. Computer mathematical modeling 2.1. Introduction to Modeling 2.2. Computer toolkit mathematical modeling(Excel, MathCad, VBA, Pascal) 2.3. Optimal planning processes modeling 2.4. Computer Simulation Applications
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"Modeling and development of information systems" Objectives of studying the section General development and the formation of the worldview of students. The main ideological component of the content of this section course is the formation of a systematic approach to the analysis of the surrounding reality. Mastering the basics of methods for constructing information help systems... Students gain an understanding of the stages in the development of an information system: the design stage and the implementation stage. The creation of a multi-tabular database takes place in the MS Access relational DBMS environment. Students master the techniques of building a database, applications (queries, reports), interface elements ( dialog boxes). Development and professionalization of computer skills. Skills acquired in basic course find further development... - work with the vector graphics when building structural models of systems - in-depth study of the capabilities of the MS Access DBMS - using MS Excel as a means of working with a database - programming in VBA in Excel environment for interface development - when working on abstracts, it is recommended to use Internet resources; prepare material for protection in the form of a presentation (Power Point)
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Project-based teaching method Problem statement: Subject area: secondary school Project goal: creation of an information system " Studying proccess»The purpose of the information system: to inform users: About the student body of the classes About the teaching staff of the school About the distribution of the teaching load and class leadership About the progress of students
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Application development Applications: queries, reports Task. It is required to obtain a list of all girls in the ninth grade who have an A in computer science. Subschema Concept Using a Hypothetical Query Language Choice STUDENTS LAST NAME STUDENTS NAME STUDENTS CLASS STUDENT CLASS = '9? sort STUDENTS.LAST NAME ascending
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VBA Programming Private Sub CommandButton1_Click () "Description of variables Dim i, j, n As Integer Dim Flag As Boolean" Data initialization Flag = False "Determines the number of rows in the list of schools n = Range (" A3 "). CurrentRegion.Rows. Count "Search in the list for the school number specified in the input field 'TextBox1" For i = 3 To n + 2 If Cells (i, 1) .Value = Val (UserForm1.TextBox1.Text) Then Flag = True Exit For End If Next Fragment of the program for processing the event "Click on the SEARCH button"
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"Computer mathematical modeling" Objectives of studying the section Mastering modeling as a method of cognizing the surrounding reality (scientific research nature of the section) - it is shown that modeling in different fields of knowledge has similar features, often for different processes it is possible to obtain very similar models; - demonstrates the advantages and disadvantages of a computer experiment in comparison with a full-scale experiment; - it is shown that both the abstract model and the computer provide an opportunity to cognize the surrounding world, to control it in the interests of man. Development of practical skills computer simulation... The general methodology of computer mathematical modeling is given. On the example of a number of models from different areas science and practice practically implements all stages of modeling, from the formulation of the problem to the interpretation of the results obtained in the course of a computer experiment. Promoting vocational guidance for students. Revealing the student's aptitude for research activities, development of creative potential, orientation towards choosing a profession related to scientific research... Overcoming subject dissociation, knowledge integration. The course examines models from various fields of science using mathematics. Development and professionalization of computer skills. Mastering software for general and specialized purposes, programming systems.
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Optimal planning process modeling Plant scheduling problem Maintenance Problem Statement Let a car service station perform two types of service: TO-1 and TO-2. Cars are accepted at the beginning of the working day and handed over to customers at the end. Due to the limited parking space, no more than 140 cars can be serviced per day. The working day lasts 8 hours. If all cars passed only TO-1, then the capacity of the station would allow servicing 200 cars per day, if all cars went through only TO-2, then 50. The cost (for the client) of TO-2 is twice as high as that of TO-1. In reality, some of the cars pass TO-1, and some, on the same day, pass TO-2. It is required to draw up such a daily service plan in order to provide the enterprise with the greatest cash receipts.
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Modeling the processes of optimal planning Formalization and mathematical model of the problem Planned indicators x - daily production plan TO-1; y - daily production plan for TO-2. The system of inequalities follows from the problem statement.The greatest profit will be achieved when maximum value functions The function f (x, y) is called the objective function, and the system of inequalities is called the system of constraints. Received a task linear programming
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Modeling the processes of optimal planning Methods for solving the linear programming problem Simplex method - universal way solutions of a linear programming problem Simplex-table Basis of St. x1 ¼ xi ¼ xr xr + 1 ¼ xj ¼ xn x1 b1 1 ¼ 0 ¼ 0 a1, r + 1 ¼ a1j ¼ a1n xi bi 0 1 ¼ 0 ai, r + 1 ¼ aij ¼ ain ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ xr br 0 0 ¼ 1 ar, r + 1 ¼ arj ¼ Arn f 0 0 0 ¼ 0 gr + 1 ¼ gj ¼ gn
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Modeling optimal planning processes Private Sub CommandButton1_Click () Dim d (5, 9) As Variant Dim i, j, r, n, k, m As Integer Dim p, q, t As String Dim a, b As Double For i = 1 To 5 For j = 1 To 9 d (i, j) = Range ("a6: i10"). Cells (i, j) .Value Next j Next in = 7: r = 3 "Analysis of the optimality of the current solution 't = "next" Do While t = "next" Simplex method program in VBA for Excel (fragment)
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Modeling the processes of optimal planning The task of planning work on the construction of the road Problem statement There are two points - the initial H and the final K; from the first to the second it is necessary to build a road, which consists of vertical and segments. The cost of building each of the possible sections is known (shown in the figure). In reality, the road will be some broken line connecting points H and K. It is required to find such a line that has the lowest cost. This is the task dynamic programming
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Computer simulation Apparatus used mathematical statistics Random events: - time interval between two transactions - transaction service time Probability density distribution functions random events Uniform distribution Gaussian normal distribution Poisson distribution
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Planned learning outcomes for EC. Students should know: the purpose and composition of information systems; stages of creating a computer information system; basic concepts of systemology existing varieties system models; what is an infological model subject area; what is a database (DB); database classification; structure relational base data (RDB); normalization of the database; what is a DBMS; how links are organized in a multi-tabular database; what are the types of queries to the database; what is the structure of the request command for selecting and sorting data; what possibilities for working with databases does a spreadsheet processor (MS Excel) have; how you can create and execute a macro in MS Excel; what is an object-oriented application; VBA programming basics; the content of the concepts "model", " information model"," Computer mathematical model ";
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stages of computer mathematical modeling, their content; the composition of the toolkit for computer mathematical modeling; capabilities of tabular Excel processor in the implementation of mathematical modeling; the capabilities of the MathCAD system in the implementation of computer mathematical models; the specifics of computer mathematical modeling in economic planning; examples of meaningful tasks from the field of economic planning, solved by the method of computer modeling; statement of problems solved by the linear programming method; statement of problems solved by the method of dynamic programming; basic concepts of probability theory required for implementation simulation: random variable, law of distribution of a random variable, probability density of distribution, reliability of the result of a statistical study; ways to obtain sequences of random numbers with a given distribution law; statement of problems solved by the method of simulation modeling in the theory of queuing.
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Students should be able to: design a simple information and reference system; design a multi-tabular database; navigate the MS Access DBMS environment; create a database structure and fill it with data; exercise in MS Access queries fetching using the query designer; work with forms; make inquiries with the receipt of the final data; receive reports; organize single-table databases (lists) in MS Excel; select and sort data in lists; filter data; create pivot tables; record macros for MS Excel using a macro recorder; write simple programs event handling in VBA. apply the scheme of a computer experiment when solving meaningful problems where there is a need for computer mathematical modeling; select the factors that influence the behavior of the system under study, perform the ranking of these factors;
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build models of the studied processes; to choose software to study the constructed models; analyze the results obtained and investigate the mathematical model for different sets parameters, including boundary or critical; use simple optimization economic models; build the simplest models of queuing systems and interpret the results. implement simple mathematical models on a computer, creating algorithms and programs in the language Visual basic; use the capabilities of TP Excel to carry out simple mathematical calculations and illustrating the results of mathematical modeling with graphs and bar charts; use the tool "Search for a solution" TP Excel to solve linear and nonlinear programming problems; use the MathCAD system to carry out simple mathematical calculations, graphically illustrate the results of modeling; use the MathCAD system to solve linear and nonlinear optimization problems.
"Computer mathematical modeling" Objectives of studying the section. Mastering modeling as a method of cognizing the surrounding reality (scientific research nature of the section) - it is shown that modeling in various fields of knowledge has similar features, often for different processes it is possible to obtain very similar models; - demonstrates the advantages and disadvantages of a computer experiment in comparison with a full-scale experiment; - it is shown that both the abstract model and the computer provide an opportunity to cognize the surrounding world, to control it in the interests of man. Development of practical skills in computer modeling. The general methodology of computer mathematical modeling is given. On the example of a number of models from various fields of science and practice, practically all stages of modeling are implemented, from the formulation of the problem to the interpretation of the results obtained in the course of a computer experiment. Promoting vocational guidance for students. Revealing the student's aptitude for research activities, the development of creative potential, orientation towards the choice of a profession related to scientific research. Overcoming subject dissociation, knowledge integration. The course examines models from various fields of science using mathematics. Development and professionalization of computer skills. Mastering software for general and specialized purposes, programming systems.
The concept of a model is key in general systems theory. Modeling as a powerful - and often the only - research method implies the replacement of a real object with another - material or ideal.
The most important requirements for any model are its adequacy to the object under study within specific task and feasibility with the available means.
In efficiency theory and computer science, a model of an object (system, operation) is a material or ideal (mentally imaginable) system created and / or used in solving a specific problem in order to obtain new knowledge about the original object, adequate to it in terms of the studied properties and more simpler than the original in other respects.
The classification of the main modeling methods (and their corresponding models) is shown in Fig. 3.1.1.
In the study of economic information systems (EIS), all modeling methods are used, however, this section will focus on semiotic (sign) methods.
Recall that semiotics (from the Greek semeion - sign, sign) is the science of general properties sign systems, that is, systems of concrete or abstract objects (signs), with each of which a certain value is associated. Examples of such systems are any languages
Rice. 3.1.1. Classification of modeling methods
(natural or artificial, for example, data description or modeling languages), signaling systems in society and the animal world, etc.
Semiotics includes three sections: syntactics; semantics; pragmatics.
Syntactics studies the syntax of sign systems without regard to any interpretations and problems associated with the perception of sign systems as means of communication and communication.
Semantics studies the interpretation of statements of a sign system and, from the point of view of modeling objects, occupies the main place in semiotics.
Pragmatics examines the attitude of the person using the sign system to the sign system itself, in particular - the perception of meaningful expressions of the sign system.
Of the many semiotic models due to the greatest distribution, especially in the context of informatization modern society and the introduction of formal methods into all spheres of human activity, we will single out mathematical ones that reflect real systems via mathematical symbols... At the same time, taking into account the fact that we are considering modeling methods in relation to the study of systems in various operations, we will use the well-known methodology system analysis, efficiency theory and decision making.
More on the topic 3. TECHNOLOGY OF SIMULATION OF INFORMATION SYSTEMS Methods of modeling systems:
- Simulation models of economic information systems Methodological foundations of the application of the method of simulation
- Section III BASES FOR MODELING A SERVICE MARKETING SYSTEM
- CHAPTER 1. CONTROLLED DYNAMIC SYSTEMS AS A COMPUTER SIMULATION OBJECT
- Fundamentals of structural modeling of the marketing system of medical services
- Section IV EXAMPLE OF APPLIED USE OF A MARKETING SYSTEM MODEL IN IMITATION MODELING
- The concept of modeling the financial sphere of marketing systems
Tasks and functions of the information system.
IS can solve two groups of problems. The first group is associated with purely information support of the main activity (selection of the necessary messages, their processing, storage, search and delivery to the subject of the main activity with a predetermined completeness, accuracy and efficiency in the most acceptable form). The second group of tasks is associated with the processing of the received information / data in accordance with certain algorithms in order to prepare solutions to the tasks facing the subject of the main activity. To solve such problems, the IS must have necessary information about the subject area. To solve such problems, the IS must have a certain artificial or natural intelligence. Information system - a system for supporting and automating intellectual work - search, administration, expertise and expert assessments or judgments, decision-making, management, recognition, knowledge accumulation, training. The tasks of the first group are the tasks of informatization of society "in breadth".
Tasks of the second group - tasks of informatization
society "deep".
To solve the assigned tasks, the IS must perform following functions:
selection of messages from the internal and external environment, necessary for the implementation of the main activity;
input of information into IS;
storing information in the memory of the IS, updating it and maintaining its integrity;
processing, searching and issuing information in accordance with the requirements set by the ODS. Processing can also include the preparation of options for solving user applied problems.
Information system (IS) is an interconnected set of tools, methods and personnel used for storing, processing and issuing information in order to achieve the set goal. The modern understanding of the information system involves the use of a personal computer as the main technical means of information processing. IS is an environment, the constituent elements of which are computers, computer networks, software products, DB, people, various kinds of technical and software communications, etc. Although the very idea of IP and some principles of their organization arose long before the advent of computers, however, computerization increased the efficiency of IP by tens and hundreds of times and expanded the scope of their application.
The functional structure of the information system.
In IS, it is advisable to distinguish three independent functional subsystems.
Information selection subsystem. The information system can process / process only the information that is entered into it. The quality of IP is determined not only by its ability to find and process necessary information in its own array and issue it to the user, but also the ability to select relevant information from the external environment. Such selection is carried out by the information selection subsystem, which accumulates data on the information needs of IS users (internal and external), analyzes and organizes this data, forming an IS information profile.
The subsystem for input, processing / processing and storage of information transforms input information and requests, organizes their storage and processing in order to meet the information needs of IS subscribers.
The implementation of the functions of this subsystem assumes the presence of an apparatus for describing information (coding systems, data description language (DL), etc.), organizing and maintaining information (logical and physical organization, procedures for maintaining and protecting information, etc.), a processing apparatus and information processing (algorithms, models, etc.).
The subsystem for the preparation and issuance of information directly implements the satisfaction of information needs of IS users (internal and external). To accomplish this task, the subsystem conducts a study and analysis of information needs, determines the forms and methods of their satisfaction, the optimal composition and structure of output information products, organizes the process of information support and maintenance itself.
The implementation of these functions requires an apparatus for describing and analyzing information needs and their expression in the IS language (including LOD, IPL, indexing language, etc.), as well as an apparatus for information support itself (procedures for searching and issuing information, data manipulation languages etc.). Many functions of IC subsystems are duplicated or overlapped, which is the subject of optimization in IC design. In this regard, the automation of IS is accompanied by a redistribution of IS elements.
Automation presupposes a formalized representation (structuring) of both the IS functions and the information processed in the IS itself, which allows the input, processing / processing, storage and retrieval of information using a computer. Any formalization is characterized by one or another level of adequacy of the created image of reality (model) of reality itself. Moreover, the adequacy of the model of reality is determined both by the properties of reality itself and by the capabilities of the apparatus used for its formalized representation.
From this point of view, the "level of automation" of IS is closely related to the "degree of structuredness" of information. There are three levels of structuring information: Rigidly structured information (data) - information, the formalized representation of which by modern means of its structuring (in particular, data description languages) does not lead to the loss of the adequacy of the information model itself
information. Weakly structured information is information, the formalized presentation of which leads to significant losses in the adequacy of the information model of the initial information itself.
Unstructured information is information for which currently there are no means of its formalized presentation with an acceptable level of adequacy in practice. The means of presenting such information must have high semantic and expressive abilities. The development of such tools is currently time is running along the line of creating languages for describing knowledge and IPL with high semantic power.
Information systems construction methodologies.
The industry for the development of automated information management systems originated in the 1950s - 1960s and by the end of the century acquired completely finished forms.
At the first stage, the main approach in the design of IS was the "bottom-up" method, when the system was created as a set of applications that are most important at the moment to support the activities of the enterprise. This approach is partly retained today. Within the framework of "patchwork automation", support for individual functions is quite well provided, but there is practically no strategy for the development of an integrated automation system
The next stage is associated with the realization of the fact that there is a need for fairly standard software tools for automating the activities of various institutions and enterprises. From the whole range of problems, the developers have identified the most noticeable: automation of accounting and analytical accounting and technological processes... Systems began to be designed "top-down", i.e. on the assumption that one program must meet the needs of many users.
The very idea of using a universal program imposes significant restrictions on the ability of developers to form the structure of the database, screen forms, and the choice of calculation algorithms. The rigid framework imposed "from above" does not make it possible to flexibly adapt the system to the specifics of the activity of a particular enterprise. Thus, the material and time costs for the implementation of the system and its fine-tuning to the customer's requirements usually significantly exceed the planned indicators.
According to statistics compiled by the Standish Group (SSL), of the 8,380 projects surveyed by SSL in 1994, more than 30% of projects with a total value of more than $ 80 billion failed. At the same time, only 16% of the the total projects, and cost overruns amounted to 189% of the planned budget.
At the same time, IS customers began to put forward more and more requirements aimed at ensuring the possibility of complex use of corporate data in the management and planning of their activities. Thus, an urgent need arose to form a new methodology for building information systems.
According to modern methodology, the process of creating an IS is a process of building and sequential transformation of a number of agreed models at all stages of the IS life cycle (LC). At each stage of the life cycle, specific models are created - organizations, requirements for
IP. IP project. application requirements, etc. Usually, the following stages of creating an IS are distinguished: the formation of requirements for the system, design, implementation, testing, commissioning, operation and maintenance.
The initial stage of the process of creating an IS is the modeling of business processes occurring in the organization and realizing its goals and objectives. The organization model, described in terms of business processes of business functions, allows you to formulate the basic requirements for IS.
The IS design is based on domain modeling. In order to obtain an IS project adequate to the subject area in the form of a system of correctly working programs, it is necessary to have an integral, systematic representation of the model, which reflects all aspects of the functioning of the future information system. In this case, a domain model is understood as a certain system that imitates the structure or functioning of the studied domain and meets the basic requirement - to be adequate to this domain.
Preliminary modeling of the subject area allows you to reduce the time and terms of design work and get a more efficient and high-quality project. Without modeling the domain, there is a high probability of the assumption a large number mistakes in solving strategic issues, leading to economic losses and high costs for the subsequent redesign of the system. As a result, all modern IS design technologies are based on the use of domain modeling methodology.
The following requirements are imposed on domain models:
Formalization, providing an unambiguous description of the structure of the subject area;
Comprehensibility for customers and developers based on the use of graphic means of displaying the model;
Realizability, implying the availability of means of physical implementation of the domain model and IS;
Providing an assessment of the effectiveness of the implementation of the domain model based on certain methods and calculated indicators.
Functional modeling IDEF0: basic definitions and provisions.
The program of integrated computerization of production ICAM (ICAM - Integrated Computer Aided Manufacturing) is aimed at increasing the efficiency of industrial enterprises through the widespread introduction of computer (information) technologies. In the United States, this circumstance was realized back in the late 70s, when the US Air Force proposed and implemented
The IDEF (ICAM Definition) methodology makes it possible to study the structure, parameters and characteristics of production-technical and organizational-economic systems (in the future, where it does not cause confusion - systems). The general IDEF methodology consists of three specific modeling methodologies based on graphical representations of systems:
IDEF0 is used to create a functional model that displays the structure and functions of the system, as well as the flows of information and material objects connecting these functions.
IDEF1 is used to build an information model that displays the structure and content of information flows required to support the system's functions;
IDEF2 allows you to build a dynamic model of the time-varying behavior of functions, information and system resources.
By now, the most widespread and applied methodologies are IDEF0 and IDEF1 (IDEF1X), which have received the status of federal standards in the United States. The IDEF0 methodology, the features and application of which are described in this Guidance Document (GD), is based on the approach developed by Douglas T. Ross in the early 70s and called SADT (Structured Analysis & Design Technique - method of structural analysis and design). The approach and, as a consequence, the IDEF0 methodology are based on a graphical language for describing (modeling) systems, which has the following properties.
For the correct display of the interactions of IS components, it is important to carry out joint modeling of such components, especially from the content point of view of objects and functions.
The methodology of structural systems analysis greatly helps in solving such problems.
Structural analysis is usually called a method of studying a system, which begins with its general overview, and then details, acquiring hierarchical structure with all a large number levels. Such methods are characterized by: division into levels of abstraction with a limited number of elements (from 3 to 7); bounded context, including only the essential details of each level; use of strict formal recording rules; consistent approximation to the result.
Let's define the key concepts of the structural analysis of the enterprise (organization).
Operation is an elementary (indivisible) action performed at one workplace.
Function - a set of operations grouped according to a specific feature.
A business process is a related set of functions, during the execution of which certain resources are consumed and a product is created (object, service, scientific
discovery, idea), which is of value to the consumer.
A sub-process is a business process that is a structural element of some business process and is of value to the consumer.
A business model is a graphically structured description of a network of processes and operations associated with data, documents, organizational units and other objects that reflect the existing or intended activities of the enterprise. There are various methodologies for structural domain modeling, among which function-oriented and object-oriented methodologies should be distinguished.
Describing a system using IDEF0 is called a functional model. The functional model is intended to describe existing business processes, which uses both natural and graphical languages. To convey information about a specific system, the source of the graphical language is the IDEF0 methodology itself.
The IDEF0 methodology prescribes the construction of a hierarchical system of diagrams - single descriptions of system fragments. First, the system as a whole and its interaction with the outside world are described (context diagram), after which functional decomposition is carried out - the system is divided into subsystems and each subsystem is described separately (decomposition diagrams). Then each subsystem is broken down into smaller ones, and so on until the desired level of detail is achieved.
Tool environment BPwin.
Business process modeling is usually performed using case tools. These tools include BPwin (PLATINUM technology), Silverrun (Silverrun technology), Oracle Designer (Oracle), Rational Rose (Rational Software), etc. The functionality of the structural modeling tools for business processes will be discussed using the example of the BPwin case tool.
BPwin supports three modeling methodologies: functional modeling (IDEF0); description of business processes (IDEF3); data flow diagrams (DFD). BPwin has a fairly simple and intuitive user interface. When you start BPwin, by default, the main toolbar appears, the tool palette (the appearance of which depends on the selected notation) and, on the left, the model navigator - Model Explorer).
When creating a new model, a dialog appears in which you should specify whether the model will be created anew or it will be opened from a file or from the ModelMart repository, then enter the name of the model and select the methodology in which the model will be built.
As noted above, BPwin supports three methodologies - IDEF0, IDEF3 and DFD, each of which solves its own specific problems. In BPwin, it is possible to build mixed models, that is, a model can contain both IDEF0 and IDEF3 and DFD diagrams at the same time. The composition of the tool palette changes automatically when switching from one notation to another.
A BPwin model is viewed as a collection of activities, each of which operates on a set of data. Work is depicted as rectangles, data as arrows. If you click on any object of the model with the left mouse button, a context menu appears, each item of which corresponds to the editor of a certain property of the object.
At the initial stages of creating an IP, it is necessary to understand how the organization that is going to automate works. The manager knows the work as a whole well, but is not able to delve into the details of the work of each ordinary employee. An ordinary employee knows well what is happening in his workplace, but may not know how colleagues work. Therefore, to describe the work of an enterprise, it is necessary to build a model that will be adequate to the subject area and contain the knowledge of all participants in the organization's business processes.
The most convenient language for modeling business processes is IDEF0, where the system is represented as a set of interacting activities or functions. This purely functional orientation is fundamental - the functions of the system are analyzed independently of the objects with which they operate. This allows you to more clearly model the logic and interaction of the organization's processes.
The process of modeling a system in IDEF0 begins with the creation of a context diagram, a diagram of the most abstract level of the description of the system as a whole, containing the definition of the subject of modeling, the goal and point of view on the model.
Activities refer to named processes, functions, or tasks that occur over time and have recognizable results.
Works are depicted as rectangles. All jobs must be named and defined. The name of the job must be expressed by a verbal noun denoting an action (for example, "Company activity", "Taking an order", etc.). The work "Company Activities" may have, for example, the following definition: "This is a learning model describing company activities." When creating a new model (menu File / New), a context diagram is automatically created with a single work depicting the system as a whole.
Arrows describe the interaction of jobs and represent some kind of information expressed in nouns. (For example, "Customer Calls", "Rules and Procedures", "Accounting System".)
Textbook for universities
2nd ed., Rev. and add.
2014 G.
Circulation 1000 copies.
Format 60x90 / 16 (145x215 mm)
Execution: paperback
ISBN 978-5-9912-0193-3
BBK 32.882
UDC 621.395
Grif UMO
Recommended by UMO for Education in Telecommunications as study guide for university students educational institutions, studying in the specialties "Networks and Switching Systems", "Multichannel Telecommunication Systems"
annotation
Algorithms for modeling discrete and continuous random variables and processes. The principles and algorithms of modeling are outlined. information signals, described by Markov processes with discrete and continuous time. Principles of modeling queuing systems are considered. The features of the description and use of fractal and multifractal processes for modeling telecommunication traffic are described. Methods and examples of modeling information systems using specialized packages of applied Matlab programs, Opnet, Network simulator.
For students enrolled in the specialties "Networks and Switching Systems", "Multichannel Telecommunication Systems", "Information Systems and Technologies".
Introduction
1 General principles system modeling
1.1. General concepts models and simulations
1.2. Model classification
1.3. Model structure
1.4. Methodological foundations of formalizing the functioning of a complex system
1.5. Component Modeling
1.6. Stages of the formation of a mathematical model
1.7. Simulation modeling
Control questions
2 General principles of building communication systems and networks
2.1. The concept of building communication systems and networks
2.2. Layered network models
2.2.1. Three-tier model
2.2.2. TCP / IP protocol architecture
2.2.3. Reference model OSI
2.3. Communication network structure
2.3.1. Global networks
2.3.2. Local area networks
2.3.3. Computer network topologies
2.3.4. Local networks Ethernet
2.4. Frame Relay networks
2.5. IP telephony
Control questions
3 Simulation of random numbers
3.1. General information about random numbers
3.2. Programming methods generating uniformly distributed random numbers
3.3. Formation of random variables with a given distribution law
3.3.1. Inverse function method
3.3.2. Approximate methods for converting random numbers
3.3.3. Screening Method (Neumann Generation Method)
3.4. Methods Based on the Central Limit Theorem
3.5. Algorithms for Modeling Frequently Used Random Variables
3.6. Algorithms for Modeling Correlated Random Variables
3.7. Formation of realizations of random vectors and functions
3.7.1. Modeling an n-dimensional random point with independent coordinates
3.7.2. Formation of a random vector (within the framework of the correlation theory)
3.7.3. Formation of realizations random functions
4 Modeling discrete distributions
4.1. Bernoulli distribution
4.2. Binomial distribution
4.3. Poisson distribution
4.4. Simulation of trials in a random event scheme
4.4.1. Simulation of random events
4.4.2. Modeling opposite events
4.4.3. Modeling a discrete random variable
4.4.4. Modeling full group events
4.5. Streams of events
4.6. Processing of simulation results
4.6.1. Precision and number of realizations
4.6.2. Primary statistical data processing
Control questions
5 Algorithms for modeling stochastic signals and interference in communication systems
5.1. Algorithm for modeling non-stationary random processes
5.2. Algorithms for modeling stationary random processes
5.3. Methods for modeling signals and noise in the form of stochastic differential equations
5.4. Examples of models of stochastic processes in communication systems
5.4.1. Information process models
5.4.2. Interference Models
5.4.3. Characteristics of the main types of interference
Control questions
6 Markov stochastic processes and their modeling
6.1. Basic concepts of a Markov stochastic process
6.2. Basic properties of discrete Markov chains
6.3. Continuous Markov Chains
6.3.1. Basic concepts
6.3.2. Semi-Markov processes
6.3.3. Death and reproduction processes
6.4. Models of continuous-valued Markov random processes based on stochastic differential equations
6.5. Modeling Markov Stochastic Processes
6.5.1. Modeling discrete processes
6.5.2. Modeling scalar continuous-valued processes
6.5.3. Modeling continuous-valued vector processes
6.5.4. Modeling a Gaussian process with fractional-rational spectral density
6.5.5. Modeling multiply connected sequences
6.5.6. Modeling Markov Processes Using Shaping Filters
6.5.7. Algorithm for statistical modeling of Markov chains
Control questions
7 Examples of Markov models
7.1. Markov models of speech dialogue of subscribers
7.1.1. Speech states
7.1.2. Dialogue models
7.2. Markov models of speech monologue
7.3. Markov-driven Poisson process in speech models
7.4. Markov models of digital sequences at the output of the G.728 codec
7.5. Statistical multiplexing of the source of speech packets taking into account the Markov model of telephone dialogue
7.6. Markov model wireless channel with ARQ / FEC mechanism
7.7. Batching errors
7.8. Calculation of error stream characteristics by model parameters
7.8.1. Estimating the parameters of the error stream
7.8.2. Evaluation of the adequacy of the error flow model
7.9. Markov models for assessing the QoS of real-time multimedia services on the Internet
7.9.1. Real-time multimedia services concept
7.9.2. Analysis and modeling of delays and losses
7.10. Models of multimedia traffic streams
Control questions
8 Queuing systems and their modeling
8.1. general characteristics queuing systems
8.2. Queuing system structure
8.3. Queuing systems with waiting
8.3.1. Service system M / M / 1
8.3.2. Service system M / G / 1
8.3.3. Networks with a large number of nodes connected by communication channels
8.3.4. Priority service
8.3.5. Service system M / M / N / m
8.4. Failure queuing systems
8.5. General principles of modeling queuing systems
8.5.1. Statistical test method
8.5.2. Block models systems functioning processes
8.5.3. Features of modeling using Q-circuits
Control questions
9 Modeling information systems using typical technical means
9.1. System modeling and programming languages
9.2. Understanding the GPSS Language
9.2.1. Dynamic objects GPSS. Transaction-oriented blocks (operators)
9.2.2. Hardware-oriented blocks (operators)
9.2.3. Multichannel service
9.2.4. GPSS Statistical Blocks
9.2.5. Operating units GPSS
9.2.6. Other GPSS blocks
9.3. Simulation modeling ETHERNET networks in GPSS environment
Control questions
10 Modeling of information transmission systems
10.1. Typical data transmission system
10.2. Transmission immunity discrete signals... Optimal reception
10.3. Estimation of the probability of erroneous reception of discrete signals with fully known parameters
10.4. Immunity of discrete signals with random parameters
10.5. Immunity of discrete signals with incoherent reception
10.6. Immunity of discrete signals with random essential parameters
10.7. Algorithms for the formation of discrete signals
10.8. Algorithm for generating interference
10.9. Algorithm for demodulation of discrete signals
10.10. The structure of the imitation complex and its subroutines
10.11. Software environment Mathworks Matlab and Simulink visual modeling package
10.11.1. Technical description and interface
10.11.2. Simulink Visual Simulation Package
10.11.3. Subsystem creation and masking
10.11.4. Communications Toolbox Extension Pack
10.12. Modeling the blocks of the WiMAX data transmission system
10.12.1. Transmitter simulation
10.12.2. Modeling a transmission channel
10.12.3. Receiver simulation
10.12.4. Implementation of the model in the Mathlab system
10.13. Results of WiMAX System Simulation
Control questions
11 Self-similar processes and their application in telecommunications
11.1. Fundamentals of the theory of fractal processes
11.2. Multifractal processes
11.3. Estimation of the Hurst exponent
11.4. Multifractal analysis using software
11.4.1. Description of the software
11.4.2. Examples of assessing the degree of self-similarity
11.5. Algorithms and software for multifractal analysis
11.6. Influence of self-similarity of traffic on the characteristics of the service system
11.7. Methods for modeling self-similar processes in TV traffic
11.8. Exploring the Self-Similar Structure of Ethernet Traffic
11.9. Self-similar traffic congestion control
11.10. Fractal Brownian motion
11.10.1. RMD algorithm for generating FBD
11.10.2. SRA FWA Generation Algorithm
11.12. Fractal Gaussian noise
11.12.1. FFT algorithm for FGS synthesis
11.12.2. Evaluation of simulation results
Control questions
12 Modeling a telecommunications network node
12.1. Fundamentals of Frame Relay Protocol
12.2. Site design Frame networks Relay
12.3. Simulation results of an FR router with G.728 codecs at the input
12.4. Impact of self-similarity of traffic on QoS
Control questions
13 Specialized systems simulation of computer networks
13.1. General characteristics of specialized packages application programs network modeling
13.2. General principles of modeling in the OPNET Modeler environment
13.3. OPNET Application Examples
13.3.1. Model for assessing the quality of service
13.3.2. Implementation of the local network model
Control questions
14 Simulation with Network simulator 2
14.1. History and architecture of the NS2 package
14.2. Create a simulator object
14.3. Creating a network topology
14.4. Setting generator parameters
14.4.1. Exponential On / Off
14.4.2. Pareto On / Off
14.5. Two main queuing algorithms
14.6. Adaptive Routing in NS2
14.6.1. User-level API
14.6.2. Internal architecture
14.6.3. Extensions to other classes
14.6.4. Flaws
14.6.5. List of commands used to simulate dynamic scripting in NS2
14.6.6. An example of dynamic routing in NS2
14.7. Running a script program in NS2
14.8. Simulation results processing procedure
14.9. An example of modeling a wireless network
14.10. An example of transmission quality simulation streaming video using NS 2 package
14.10.1. The structure of the hardware and software complex for assessing the quality of streaming video
14.10.2. Functional modules PACK
14.10.3. Video quality assessment
