Receptive field - a set of receptors that send signals to a given neuron through one or more synapses. Mnemo pattern as a cognitive structure
Social structure - closed or bounded (they also say: countable) set. The number of substructures and the number of elements in it is limited. Social field - an infinite uncountable set. It is created not by the number of elements, but by the number of relationships and connections between them, and they are endless. Moreover, this number changes infinitely in every second of time. II. Bourdieu explains: "As I have pointed out... the field is the relationship of forces and the space of the struggle for the transformation of this totality of forces. In other words, there is competition in the field for the legitimate appropriation of what is the stake of the struggle in this field. And within the very zero of journalism, there is naturally, constant competition for the appropriation of the public, as well as for the appropriation of what should attract the public, i.e. priority for information, for scoop, for exclusives, as well as for distinctive rarities, famous names, etc. ".
The term "field" is understood by him as a relatively closed and autonomous system of social relations, i.e. it is a kind of social subspace.
Topos is a common place. In the Middle Ages, this term was used in the meaning of "the prototype of visible things." In modern mathematics, a topos is a space with a variable topology. Topology in mathematics is a skill about objects that do not change when their shape is constantly twisted or stretched. Dimensions and proportions make no sense in topology. A small oval is equal to a huge circle.
Bourdieu's first models of the social field were the intellectual, literary and religious fields. Later, other areas of the social space were added to them - politics, economics, science, sports, family.
Separate agents, groups of agents, classes and spheres of society (political, economic, religious, etc.), identified by certain properties, constitute subfields in social space. If these properties are considered not only as frozen characteristics, say, religion or level of education, but as some kind of active properties, namely social actions and interactions, then the subfields turn into fields of power. The concepts of force and interaction, which include rivalry, "practical solidarity", exchange, direct contacts and other actions, transfer the theory from the category of substantive to the category field theories.
Field Theory: A History of the Question. Field theories are most fully represented by two sciences - physics and psychology. The concept of force is based on the classical physics Newton. Faraday and Maxwell, having studied the effects of the forces of electricity and magnetism, introduced the concept of a force field and were the first to go beyond Newton's physics. The state capable of generating force has been called field. The field creates each charge, regardless of the presence of an opposite charge that can experience its effect. This discovery significantly changed the idea of physical reality. Newton believed that forces are closely related to the bodies between which they act. Now, the place of the concept of force was taken by a more complex concept of the field, which correlated with certain natural phenomena and had no correspondence in the world of mechanics. The pinnacle of this theory, called electrodynamics, was the realization that light is nothing but a high frequency alternating electromagnetic field moving through space in the form of waves. Today we know that radio waves, visible light waves, and X-rays are nothing but oscillating electromagnetic fields, differing only in the frequency of oscillations. Einstein went even further, stating that the ether does not exist, and that electromagnetic fields have their own physical nature, can move in empty space and are not phenomena from the field of mechanics. Einstein's general theory of relativity stated that three-dimensional space is indeed curved under the influence of the gravitational field of bodies with a large mass. Quantum theory has expanded our understanding of space. Quantum theory describes observable systems in terms of probabilities. This means that we can never say exactly where a subatomic particle will be at a certain moment and how this or that atomic process will occur. The experiments of the last decades have revealed the dynamic essence of the world of particles. Any particle can be transformed into another; energy can be converted into particles, and vice versa. In this world, such concepts of classical physics as "elementary particle", "material substance" and "isolated object" are meaningless. The Universe is a mobile network of inseparably connected energy processes. A comprehensive theory for describing subatomic reality has not yet been found, but already now there are several models that describe certain aspects of it quite satisfactorily.
Field theory is also psychological direction, formed under the influence of the ideas of the German-American scientist Kurt Lewin(1890–1947). Since 1933, having emigrated to the United States, he developed the concept of personality (based on the concept of a field borrowed from physics) as a unity of personality and its environment. To build a model of the personality structure and its interactions with the environment, the language of topology was used, a section of geometry that studies the relative position of figures and the distances between their elements. Since then, the zero theory of Levin and his followers has acquired a second name - topological, or vector, psychology. She claims that psychic energy is transferred from the personality to the surrounding objects, which, because of this, acquire a certain valency and begin to attract or repel it, to cause locomotion. When such behavior collides with insurmountable barriers, psychic energy is transferred to other personal systems associated with other activities, a substitution occurs. The integral structure of the human psyche appears as a personality taken with its psychological environment, on the border between which there are perceptual and motor systems. At the heart of human behavior, Levin believed, is a force that has a direction and can be represented by a vector. The concept of a vector field used by K. Levin means an area, at each point P which is given a vector a(P). Many physical phenomena and processes lead to the concept of a vector field (for example, the velocity vectors of particles of a moving fluid at each moment of time form a vector field). Lewin attached particular importance to cognitive power, which is restructured in the course of the implementation of behavior.
concept fields plays no lesser role in P. Bourdieu than the category of space. He interprets space as a field of forces, or rather as a set of objective relations of forces that are imposed on everyone entering it, and which are irreducible to the intentions of individual agents, as well as to their interaction. In other words, the concept of the social field is subject to the well-known principle from systems theory "the whole is not reduced to the sum of its parts."
Indeed, the behavior of each of us is forcibly influenced by such forces as the power of money, the traditions of the environment, the level and profile of education. We may not want their impact on us, but we cannot disobey them. They have an objective character, and their configuration and vectors are formed somewhere above us and behind our backs. The political system of society is beyond our control, we have almost no influence on it, our vote in elections is a microscopically insignificant value. Political parties, as well as large corporations, negotiate behind our backs and create such a configuration of influence vectors that is beneficial only to them, but which forces us to submit to this objective force.
Based on the teachings of P. Bourdieu, modern sociologists distinguish the following properties of the social field (Table 14.1).
The social field of P. Bourdieu is a multidimensional space of positions, each of which is determined by a set of variables depending on one or another type of capital (or their combination).
Table 14.1
Properties and signs of the social field
Properties |
signs |
Holistic nature of the field |
Within the field, social interaction is much more intense than between fields. There is an integration property |
Multifactorial nature of the field |
The behavior of an individual is the result of the influence of a large number of factors. Many interacting factors generate a systemic quality of the field, which is not reducible to the sum of the influences of all factors and resembles an unpredictable play of forces. |
Forced nature of the field |
The social field has a power character, i.e. has coercive power in relation to the people who got into it. The individual, regardless of personal tastes and needs, is forced to adapt to the requirements of his field. |
Multiple field character |
Each individual is simultaneously in several social fields. Different fields have different potential for human impact |
Resource nature of the field |
Field agents interact with each other and with representatives of another field with a force proportional to the amount of available funds, i.e. the magnitude of their power, economic, social or cultural capital |
The value character of zero |
|
Differentiated nature of the field |
The fields are formed in different planes and intertwined in an unpredictable way. Zeros have different strengths, so their effect on individuals who fall into them can vary greatly |
Comparative nature of structure and field |
The basis for the emergence of a social structure is the social division of labor, the basis of the social field is the force interaction of agents |
The nature of transitions in space and field |
The social space is discrete, it is very easy to move from one topos to another. The social field is continuous, it has the power of attraction, it is very difficult to leave its boundaries |
The nature of the socialization potential of the field |
Social space creates conditions for the socialization of the individual. The social field forms the process of socialization of the individual. The field imposes on the individual its own language, symbols, norms, way of interpreting events |
social field- the historically emerging interaction of social forces, the carriers of which can be individual agents, groups, organizations, resources, capitals, expressing themselves through the nature of the social relations that develop between them (influence, domination, pressure, subordination, competition, etc.). Field agents interact according to certain rules, occupying a strictly designated place in the social space.
If we take a closer look at the definition of the social field, we will notice its difference from the definition of social structure. It turns out that in the social field there are elements that were not in the social structure, namely, in addition to people and statuses, there are resources and capitals. In other words, the social field is more heterogeneous. It has physical components.
Field approach depicts social reality as a dynamic, internally interconnected, mobile whole.
Each field has its own bid -"the imposition of a legitimate vision of the social world". This is especially true of the so-called experts, who in all disputes consider themselves right and dictate their opinion as the only correct one. Politicians consider themselves experts in public affairs and judge everything categorically, the elders believe that, having lived a long life, they have the right to advise the young how to behave in a given situation. Scientists dominate the profane, the locals look arrogantly at visitors. "The stake in the discussion of two politicians attacking each other with numbers is to present their vision of the political world as justified: based on objectivity, since it has real referents, and rooted in social reality, since it is confirmed by those who take it personally and upholds"
Semantic field - a set of linguistic units united by some common (integral) semantic feature; in other words, having some common nontrivial value component. Initially, the role of such lexical units was considered as units of the lexical level - words; later, descriptions of semantic fields appeared in linguistic works, including also phrases and sentences.
One of the classic examples of a semantic field is a color naming field consisting of several color ranges ( red– pink – pinkish – crimson; blue – blue – bluish –turquoise etc.): the common semantic component here is "color".
The semantic field has the following main properties:
1. The semantic field is intuitively understandable to a native speaker and has a psychological reality for him.
2. The semantic field is autonomous and can be singled out as an independent language subsystem.
3. The units of the semantic field are connected by certain systemic semantic relations.
4. Each semantic field is connected with other semantic fields of the language and together with them forms a language system.
The field stands out nucleus, which expresses the integral seme (archiseme) and organizes the rest around itself. For example, field - human body parts: head, hand, heart- the core, the rest are less important.
The theory of semantic fields is based on the idea of the existence of certain semantic groups in the language and the possibility of the occurrence of language units in one or more such groups. In particular, the vocabulary of a language (lexicon) can be represented as a set of separate groups of words united by various relationships: synonymous (boast - brag), antonymous (speak - be silent), etc.
The elements of a separate semantic field are connected by regular and systemic relations, and, consequently, all the words of the field are mutually opposed to each other. Semantic fields may intersect or completely enter one into the other. The meaning of each word is most fully determined only if the meanings of other words from the same field are known.
A single linguistic unit can have several meanings and, therefore, can be assigned to different semantic fields. For example, the adjective red can be included in the semantic field of color designations and at the same time in the field, the units of which are united by the generalized meaning "revolutionary".
The simplest kind of semantic field is field of paradigmatic type, the units of which are lexemes belonging to the same part of speech and united by a common categorical seme in meaning, between units of such a field of connection of a paradigmatic type (synonymous, antonymic, genus-species, etc.) Such fields are often also called semantic classes or lexico-semantic groups. An example of a minimal semantic field of a paradigmatic type is a synonymous group, for example, the group verbs of speech. This field is formed by verbs talk, tell, talk, talk and others. The elements of the semantic field of verbs of speech are united by the integral semantic sign of "speaking", but their meaning not identical.
The lexical system is most fully and adequately reflected in the semantic field - a lexical category of a higher order. Semantic field - it is a hierarchical structure of a set of lexical units united by a common (invariant) meaning. Lexical units are included in a certain SP on the basis that they contain the archiseme that unites them. The field is characterized by a homogeneous conceptual content of its units, so its elements are usually not words that correlate their meanings with different concepts, but lexico-semantic variants.
The entire vocabulary can be represented as a hierarchy of semantic fields of different ranks: large semantic spheres of vocabulary are divided into classes, classes into subclasses, etc., up to elementary semantic microfields. The elementary semantic microfield is lexico-semantic group(LSG) is a relatively closed series of lexical units of one part of speech, united by an archiseme of a more specific content and a hierarchically lower order than the archiseme of the field. The most important structuring relation of elements in the semantic field is hyponymy - its hierarchical system based on genus-species relations. Words corresponding to specific concepts act as hyponyms in relation to the word corresponding to the generic concept - their hypernym, and as cohyponyms in relation to each other.
The semantic field as such includes words of different parts of speech. Therefore, the units of the field are characterized not only by syntagmatic and paradigmatic, but also by associative-derivational relations. SP units can be included in all types of semantic categorical relations (hyponymy, synonymy, antonymy, conversion, derivational derivation, polysemy). Of course, not every word by its nature enters into any of these semantic relations. Despite the great diversity in the organization of semantic fields and the specifics of each of them, we can talk about a certain structure of the joint venture, which implies the presence of its core, center and periphery (“transfer” - the core, “donate, sell” - the center, “build, cleanse” - periphery).
The word appears in the SP in all its characteristic connections and various relationships that actually exist in the lexical system of the language.
Random fields are random functions of many variables. In the future, four variables will be considered: coordinates, which determine the position of a point in space, and time. The random field will be denoted as . Random fields can be scalar (one-dimensional) and vector (-dimensional).
In the general case, a scalar field is given by the set of its -dimensional distributions
and the vector field - a set of its own - dimensional distributions
If the statistical characteristics of the field do not change when the time reference changes, i.e., they depend only on the difference, then such a field is called stationary. If the transfer of the origin does not affect the statistical characteristics of the field, i.e., they depend only on the difference, then such a field is called spatially homogeneous. A homogeneous field is isotropic if its statistical characteristics do not change when the direction of the vector changes, i.e., they depend only on the length of this vector.
Examples of random fields are the electromagnetic field during the propagation of an electromagnetic wave in a statistically inhomogeneous medium, in particular, the electromagnetic field of a signal reflected from a fluctuating target (generally speaking, this is a vector random field); volumetric radiation patterns of antennas and patterns of secondary radiation of targets, the formation of which is influenced by random parameters; statistically uneven surfaces, in particular the earth's surface and the sea surface during waves, and a number of other examples.
In this section, some issues of modeling random fields on a computer are considered. As before, the modeling task is understood as the development of algorithms for the formation of discrete field realizations on a digital computer, i.e., sets of sample values of the field
,
where - discrete spatial coordinate; - discrete time.
In this case, it is assumed that independent random numbers are the initial ones when modeling a random field. The set of such numbers will be considered as a random -correlated field, hereinafter called -field. A random -field is an elementary generalization of discrete, white noise to the case of several variables. Modeling of the -field on a digital computer is carried out very simply: the space-time coordinate is assigned a sample value of a number from a generator of normal random numbers with parameters (0, 1).
The task of digital simulation of random fields is new in the general problem of developing a system of efficient algorithms for simulating various kinds of random functions, focused on solving statistical problems of radio engineering, radiophysics, acoustics, etc. by computer simulation.
In the most general form, if the or -dimensional distribution law is known, a random field can be modeled on a computer as a random or -dimensional vector using the algorithms given in the first chapter. However, it is clear that this path, even with a relatively small number of discrete points along each coordinate, is very complicated. For example, the simulation of a flat (independent of ) scalar random field at 10 discrete points along the coordinates and and for 10 times is reduced to the formation on a computer of realizations of a -dimensional random vector.
Simplification of the algorithm and reduction in the volume of calculations can be achieved if, similarly to what was done with respect to random processes, algorithms are developed for modeling special classes of random fields.
Consider possible algorithms for modeling stationary homogeneous scalar normal random fields. Random fields of this class, just like stationary normal random processes, play a very important role in applications. Such fields are completely specified by their spatiotemporal correlation functions
(Here and in what follows, it is assumed that the mean value of the field is zero.)
An equally complete characteristic of the considered class of random fields is the function of the spectral density of the field, which is a four-dimensional Fourier transform of the correlation function (a generalization of the Wiener-Khinchin theorem):
,
where is the scalar product of the vectors and . Wherein
.
The spectral density function of a random field and the energy spectrum of a stationary random process have a similar meaning, namely: if a random field is represented as a superposition of space-time harmonics with a continuous frequency spectrum, then their intensity (total amplitude dispersion) in the frequency band and spatial frequency band is equal to .
A random field with intensity can be obtained from a random field with spectral density , if the field is passed through a space-time filter with a transfer coefficient equal to unity in the band , and equal to zero outside this band.
Spatio-temporal filters (SPFs) are a generalization of conventional (temporal) filters. Linear PVFs, like ordinary filters, are described using the impulse response
and transfer function
.
The process of linear space-time field filtering can be written as a four-dimensional convolution:
(2.140)
where is the field at the output of the PVF with an impulse transient response. Wherein
where are the spectral density functions and the correlation functions of the fields at the input and output of the PVF, respectively.
The proof of relations (2.141), (2.142) completely coincides with the proofs of similar relations for stationary random processes.
The analogy of harmonic expansion and filtering of random fields with harmonic expansion and filtering of random processes allows us to propose similar algorithms for their modeling.
Let it be required to construct algorithms for computer simulation of a stationary, space-homogeneous scalar normal field with a given correlation function or spectral density function .
If the field is given in a finite space, bounded by the limits , and is considered on a finite time interval , then to form discrete realizations of this field on a digital computer, one can use an algorithm based on the canonical expansion of the field in the space-time Fourier series and which is a generalization of algorithm (1.31):
Here, and are random mutually independent normally distributed numbers with parameters each, and the variances are determined from the relations:
where is a vector representing the limit of integration over space; - discrete frequencies of harmonics, according to which the canonical expansion of the correlation function is performed in the space-time Fourier series.
If the field expansion area is many times larger than its spatiotemporal correlation interval, then the dispersions are easily expressed in terms of the field spectral function (see § 1.6, item 3)
The formation of discrete realizations when modeling random fields using this method is carried out by directly calculating their values according to (formula (2.143), in which sample values of normal random numbers with parameters are taken as and , while the infinite series (2.143) is approximately replaced by a truncated series. Variances are calculated previously by formulas (2.144) or (2.146).
Although the considered algorithm does not allow one to form realizations of a random field that are unlimited in space and time, however, the preparatory work for obtaining it is quite simple, especially when using formulas (2.145), and this algorithm allows one to form discrete field values at arbitrary points in space and time selected area. When forming discrete realizations of a field with a constant step in one or several coordinates, it is expedient to use a recursive algorithm of the form (1.3) for the reduced calculation of trigonometric functions.
Unlimited discrete implementations of a homogeneous stationary random field can be formed using space-time sliding summation algorithms -fields, similar to sliding summation algorithms for modeling random processes. If is the impulse transient response of the PVF, which forms a field with a given spectral density function from the -field (the function can be obtained by four-dimensional Fourier transformation of the function , see § 2.2, item 2), then, subjecting the process of spatiotemporal filtering of the -field to discretization, we get
where - a constant determined by the choice of the sampling step over all variables - discrete -field.
The summation in formula (2.146) is carried out over all values for which the terms are not negligible or equal to zero.
The preparatory work for this modeling method is to find the appropriate weight function of the space-time shaping filter.
The preparatory work and the summation process in the algorithm (2.146) are simplified if the function can be represented as a product
In this case, as follows from (2.144), the correlation function of the field is a product of the form
If the factorization of the correlation function into factors of the form (2.148) is impossible in the strict sense, it can be done with a certain degree of approximation, in particular, by setting
When decomposing into a product (2.149) of spatial, correlation functions of isotropic random fields, for which , partial correlation functions and will obviously be the same. In this case, in view of the approximation of formula (2.149), the spatial correlation function will correspond, generally speaking, to some non-isotropic random field. So, for example, if is an exponential function of the form
then according to (2.149) . In this case, the given correlation function is approximated by the correlation function
. (2.151)
The random field with the correlation function (2.151) is not isotropic. Indeed, if a field with correlation function (2.150) has a constant correlation surface (the locus of space points where the field values have the same correlation with the field value at some arbitrary fixed point in space) is a sphere, then in case (2.151) the constant correlation surface is the surface of a cube inscribed in a given sphere. (The maximum distance between these surfaces can serve as a measure of the approximation error).
An example in which expansion (2.149) is exact is a correlation function of the form
Decomposition (2.149) allows us to reduce the rather complicated process of quadruple summation in algorithm (2.146) to the repeated application of a single sliding summation.
These are the basic principles of modeling normal homogeneous stationary random fields. Modeling of non-normal homogeneous stationary fields with a given one-dimensional distribution law can be done by an appropriate non-linear transformation of normal homogeneous stationary fields using the methods discussed in § 2.7.
Example 1 Let the impulse response of the spatial filter for the formation of a flat scalar time-constant field have the form
where and are discretization steps in variables and with a weight function form discrete realizations of the field. The process of such double smoothing - the field is illustrated in Fig. 2.11.
In the example under consideration, the process of moving summation can easily be reduced to a calculation in accordance with the recursive formulas (§ 2.3)
This example allows for generalizations. First, in a similar way, it is obviously possible to form realizations of more complex fields than a flat, time-constant field. Secondly, the example suggests the possibility of using recurrent algorithms for modeling random fields. Indeed, if the impulse response of the PVF, which forms a field with a given correlation function from the -field, is represented as a product of the form (2.151), then, as was shown, the formation of field realizations is reduced to the repeated application of algorithms for modeling stationary random processes with correlation functions . These algorithms can be made recurrent if the correlation functions , have the form (2.50) (stochastic processes with rational spectrum).
In conclusion, it should be noted that in this section only the basic principles of digital modeling of random fields have been considered and some possible modeling algorithms have been given. A number of issues remained untouched, for example: modeling of vector (in particular, complex), non-stationary, non-homogeneous, non-normal random fields; questions of finding the weight function of the space-time shaping filter according to the given correlation-spectral characteristics of the field (in particular, the possibility of using the factorization method for multidimensional spectral functions); examples of the use of digital models of random fields in solving specific problems, etc.
The presentation of these questions is beyond the scope of this book. Many of them are the subject of future research.
The simplest database object for storing the values of one parameter of a real object or process
5. To visually display the relationships between tables in the database, use
Value condition
Error message
Data Schema
Default value
Substitution List
6. A relational database table entry may contain
Heterogeneous information (data of different types)
Exceptionally homogeneous information (data of only one type)
Only numeric information
Only text information
7. The process of creating a database table structure includes
Grouping records by some attribute
- definition of the list of fields, types and sizes of fields
Determining the list of records and counting their number
Establishing links with already created database tables
8. According to the method of accessing database data, there are
Disk-server
Table-server
Server
Client-server
9. Set the right sequence when developing a database
Description of the subject area
Development of a conceptual model
Development of an information-logical model
Development of a physical model
10. A real or imagined object, information about which must be stored in the database and be available, is called
attitude
Essence
Representation
11. Databases that implement the network data model represent dependent data in the form
Recordsets of links between them
Record Hierarchies
Table sets
Chart collections
12. The representation of the relational data model in the DBMS is implemented in the form
Predicates
tables
trees
13. Searching for data in databases
Determining data values in the current record
Procedure for extracting data that uniquely identifies records
The procedure for selecting from a set of records a subset whose records satisfy a given condition
Procedure for defining database handles
Software and programming technologies
1. A variable is...
Description of the actions to be performed by the program
The ordinal number of the element in the array
Complete minimal semantic expression in a programming language
Functional word in a programming language
A region of memory where a value is stored
2. Violation of the form of the program record, detected during testing, leads to an error message
Local
spelling
semantic
syntactic
Grammar
Stylistic
3. One of the five main properties of the algorithm is
cyclicality
Limb
Efficiency
Adequacy
informative
4. To implement the logic of the algorithm and program from the point of view of structured programming should not be used
Sequential execution
Repetitions (cycles)
Unconditional jumps
branching
5. The Java Virtual Machine is
Handler
Compiler
Interpreter
Analyzer
6. A set of statements that perform a given action and are independent of other parts of the program source code is called
subroutine
Program section
parameters
The body of the program
7. Data markup languages are
HTML and XML
8. Implementation of cycles in algorithms
Reduces the amount of memory used by the program executing the algorithm and increases the length of records of identical instruction sequences
Reduces the amount of memory used by the program executing the algorithm and reduces the number of entries of identical instruction sequences
Increases the amount of memory used by the program executing the algorithm and reduces the number of entries of identical instruction sequences
Does not reduce the amount of memory used by the program executing the algorithm, and does not increase the length of records of identical instruction sequences
9. Of the listed
2) Assembler
5) Macro assembler
not classified as a high-level language
Only 5
Only 1
10. Scripting languages are
11. ________________ grammars are used to describe the syntax of constructions in programming languages.
unambiguous
Context sensitive
Context free
Regular
12. Cannot be consistent ________________ data representation structure
Inverted
Hash addressing
treelike
Index
13. Subroutines DO NOT
Difficulty in understanding how the program works
Simplifying program readability
Structuring the program
Reduction of the overall volume of the program
14. Compiler analysis phase cannot contain steps
parsing
Lexical analysis
Semantic analysis
Intermediate code generation
15. The description of the cycle with a precondition is the following expression
Execute a statement a specified number of times
If the condition is true, execute the statement, otherwise stop
Execute statement while condition is false
- while the condition is true, execute the statement
16. The method of writing programs that allows their direct execution on a computer is called
functional programming language
Machine language programming
Logic programming language
procedural programming language
17. Sequential enumeration method is applicable
To ordered and unordered data structures
Only to unordered data structures
Figure 2
Field types
Figure 1. Presentation of information in the database
Basic concepts
Database fields
The language of modern DBMS
The language of the modern DBMS includes subsets of commands that previously belonged to the following specialized languages:
Data description language - a high-level non-procedural language of a declarative type, designed to describe the logical structure of data.
Data Manipulation Language is a DBMS command language that provides basic operations for working with data - input, modification and selection of data by request.
Structured query language (Structured Query Language, SQL) - provides data manipulation and determination of the relational database schema, is a standard means of accessing the database server.
Ensuring the integrity of the database is a necessary condition for the successful functioning of the database. Database integrity is a property of a database, which means that the database contains complete and consistent information necessary and sufficient for the correct functioning of applications. Security is achieved in the DBMS by encryption of application programs, data, password protection, support for access levels to a separate table.
Field- the smallest named element of information stored in the database and considered as a whole.
The field can be represented by a number, letters, or a combination of them (text). For example, in a telephone directory, the fields are surname and initials, address, telephone number, i.e. three fields, all text fields (the phone number is also treated as some text).
Recording- a set of fields corresponding to one object. Thus, a subscriber of the telephone network corresponds to a record consisting of three fields.
File- a set of records related by some attribute (i.e. relation, table). Thus, in the simplest case, the database is a file.
All data in the database is divided by type. All field information belonging to the same column (domain) is of the same type. This approach allows the computer to organize the control of the input information.
Main types of database fields:
Symbolic (text). This field can store up to 256 characters by default.
Numerical. Contains numerical data in various formats used for calculations.
Date Time. Contains a date and time value.
Monetary. Includes monetary values and numeric data up to fifteen integer and four fractional digits.
Note field. It can contain up to 2^16 characters (2^16 = 65536).
Counter. A special numeric field in which the DBMS assigns a unique number to each record.
Logical. Can store one of two values: true or false.
OLE (Object Linking and Embedding) object field. This field can contain any spreadsheet object, microsoft word document, picture, sound recording, or other binary data embedded in or associated with the DBMS.
Substitution master. Creates a field that offers a choice of values from a list or containing a set of constant values.
Database fields do not just define the structure of the database - they also define the group properties of the data written to the cells belonging to each of the fields.
The main properties of database table fields are listed below using the Microsoft Access DBMS as an example:
Field name- determines how the data of this field should be accessed during automatic operations with the database (by default, field names are used as table column headings).
Field type- defines the type of data that can be contained in this field.
Field size- defines the maximum length (in characters) of data that can be placed in this field.
Field Format- determines how data is formatted in the cells belonging to the field.
input mask- defines the form in which data is entered in the field (data entry automation tool).
Signature- defines the table column heading for the given field (if the label is not specified, then the Field name property is used as the column heading).
Default value- the value that is entered into the field cells automatically (data entry automation tool).
Value condition- a constraint used to validate data entry (an entry automation tool that is typically used for data that has a numeric, currency, or date type).
Error message- a text message that is displayed automatically when you try to enter erroneous data in the field (error checking is performed automatically if the Condition on value property is set).
Required field- a property that determines the mandatory filling of this field when filling the database.
Blank lines- a property that allows the input of empty string data (it differs from the Required field property in that it does not apply to all data types, but only to some, for example, text).
Indexed field- if the field has this property, all operations related to searching or sorting records by the value stored in this field are significantly accelerated. In addition, for indexed fields, you can make it so that the values in the records will be checked against this field for duplicates, which automatically eliminates data duplication.
Since different fields may contain data of different types, the properties of the fields may differ depending on the type of data. So, for example, the list of field properties above applies primarily to fields of the text type. Fields of other types may or may not have these properties, but may add their own to them. For example, for data representing real numbers, the number of decimal places is an important property. On the other hand, for fields used to store pictures, sound recordings, video clips, and other OLE objects, most of the above properties are meaningless.