What is the difference in data information. The difference between information and data

Often, the data and information are identified, but there is a significant difference between the two terms:

● Information - knowledge concerning concepts and objects (facts, events, things, processes, ideas) in the human brain;

● Data - Representing recycled information suitable for transmission, interpretation, or processing (computer files, paper documents, entries in the information system).

The difference between data information is that:

1) Data is fixed information about events and phenomena, which are stored on certain media, and information appears as a result of data processing when solving specific tasks.

For example, various data is stored in databases, and on a specific query, the database management system issues the required information.

2) Data is information carriers, not the information itself.

3) Data is transformed into information only when a person is interested in them. A person extracts information from the data, assesses, analyzes it and according to the results of the analysis, this or that decision takes place.

The data is transformed into information by several paths:

Contextualization: We know what this data is needed;

Counting: We process the data mathematically;

Correction: We correct errors and eliminate passages;

Compression: We compress, concentrate, aggregate the data.

Thus, if it is possible to use the data to reduce the uncertainty of knowledge about any subject, then the data is transformed into information. Therefore, it can be argued that the information is the data used.

4) information can be measured. The measurement measurement measure is associated with a change in the degree of ignorance of the recipient and is based on the methods of information theory.

2. Subject - This is part of the real world, the data about which we want to reflect in the database. The subject area is infinite and contains both essential concepts and data, and not significant data at all. Thus, the importance of data depends on the selection of the subject area.

Model of the subject area. The model of the subject area is our knowledge of the subject area. Knowledge can be both in the form of informal knowledge in the brain of the expert, and are also expressed formally with any means. Experience shows that the text method of representing the model of the subject area is extremely inefficient. Much more informative and useful in database development are the descriptions of the subject area, made using specialized graphics notations. There is a large number of methods describing the subject area. From the most famous, it is possible to name the SADT structural analysis technique and the IDEF0 based on it, the Heina-Sarson data stream diagrams, the UML object-oriented analysis technique, and others, the subject area model describes the processes occurring in the subject area and the data used by these processes. From how correctly the subject area is modeled, the success of the further development of applications depends.

3. Database - presented in an objective form A combination of independent materials (articles, calculations, regulatory acts, court decisions and other similar materials) systematized in such a way that these materials can be found and processed using an electronic computing machine (computers).

Many specialists indicate a common error consisting in the incorrect use of the term "database" instead of the term "database management system", and indicate the need to distinguish these concepts.

Module 1 (1.5 credit): Introduction to economic informatics

Topic 1.1: Theoretical Basics of Economic Informatics

Topic 1.2: Technical means of information processing

Topic 1.3: System Software

Topic 1.4: Service software and base algorithmization

Economics and information

1.1. Theoretical Basics of Economic Informatics

1.1.2. Data, Information and Knowledge

Basic concepts of data, information, knowledge.

The basic concepts that are used in economic informatics include: data, information and knowledge. These concepts are often used as synonyms, but there are fundamental differences between these concepts.

The term data comes from the word DATA - fact, and information (informatio) means an explanation, the presentation, i.e. Information or message.

Data - This is a combination of information recorded on a certain carrier in the form suitable for constant storage, transmission and processing. Conversion and data processing allows you to get information.

Information - This is the result of the conversion and analysis of data. The difference in information from data is that the data is fixed information about events and phenomena, which are stored on certain media, and information appears as a result of data processing when solving specific tasks. For example, various data is stored in databases, and on a specific query, the database management system issues the required information.

There are other definitions of information, for example, information is information about objects and phenomena of the environment, their parameters, properties and condition that reduce the degree of uncertainty, incompleteness of knowledge.

Knowledge - This is recorded and proven by the practice of processed information that was used and can be repeatedly used to make decisions.

Knowledge is a type of information that is stored in the knowledge base and displays the knowledge of a specialist in a specific subject area. Knowledge is intellectual capital.

Formal knowledge can be in the form of documents (standards, standards) regulating decision-making or textbooks, instructions with a description of solving problems.

Informal knowledge is the knowledge and experience of specialists in a certain subject area.

It should be noted that the universal definitions of these concepts (data, information, knowledge) are not, they are interpreted in different ways.

Decision making are carried out on the basis of information received and knowledge available.

Making decisions - This is the choice of the best solution in a certain sense of the solution from a variety of admissible on the basis of available information.

The relationship of data, information and knowledge in the decision-making process is presented in the figure.

Fig. one.

To solve the task, the fixed data is processed on the basis of the knowledge available, then the information obtained is analyzed using existing knowledge. Based on the analysis, all permissible solutions are offered, and as a result of the choice, one best solution is made in a sense. The results of the decision replenish knowledge.

Depending on the scope of use, information may be different: scientific, technical, managing, economic, etc. Economic information is of interest to economic information.

Arguing over the question of the difference between information from the data, unwittingly think about, and do they have something in common?

We so often replace one word in some other things that we do not notice how our statements become absurd. In order not to fall into a stupid situation, it should be sorted out that each of them is indicating.

There is a challenge bond between data and information that the existence of one without the other is either impossible, or simply meaningless.

The data are the basis of information. In fact, they are just a set of characters. But after they have passed the interpretation operation with a certain perceiving system, the data becomes information.

The condition of emergence

So, information arises only if there is a certain source containing data, and, directly, the recipient. Data can be transformed into information in several ways: by counting, correction, compression, contextualization and breaking into categories.

Data are recorded at any source information. Recently, the amount of data has achieved incredible growth. This was caused by a rapid growth of the Internet.

Measure

Data can not be measured. As soon as we count the data, the processing process will begin. So, the data will automatically go into the category of "information". You can measure information. To do this, it is enough to assess the level of knowledge before the receipt of information and after it.

The result of the conversion

The human brain, like the most perfect computer, processes the data we received and gives some information. And when it comes to apply it to another thought process, this information in turn becomes data from which new information will be received.

The final stage of information transformation that has passed repeated processing during a certain period of time, knowledge becomes.

Conclusions Site

1. Data and information are closely interconnected.
2. The data is fixed, they really exist in each time one unit. Information occurs only when processing this data.
3. Data after transformation becomes information. Multiplely proven information - knowledge.
4. Information, in contrast to the data, the substance is measured.

Data- This is also knowledge, however, the knowledge of a very special kind. In the first approximation, the data is the result of the linguistic fixation of single observation, experiment, fact or situation. Examples of data can be:

a) "Such a number, such a year, at the time t in a certain area it was raining" (meteorological given) ";

b) "The price of businesswood on such a day of such a year, according to such an exchange, was as many dollars per ton" (trading);

c) "The state budget deficit in such a country was as many billions of dollars in such a year" (financial this year);

d) "At such a point in time, an automatic laboratory, heading for Jupiter, deviated from the calculated trajectory on so much degrees, so many thousands of kilometers in such a direction" (data from the sphere of space technology).

From a technological point of view, some experts The concept of "data" is usually defined as information that is stored in databases and is processed by application programs, or the information presented as a sequence of symbols and intended for processing in a computer, i.e. Data includes only the part of knowledge that is formalized to such an extent that formalized processing procedures can be carried out over them with various technical means.

Data is the information presented in a formalized form suitable for automatic processing with possible human participation. Data is the information written (encoded) in the machine language.. Data is separate facts characterizing objects, processes and phenomena in the subject area, as well as their properties.

There is a difference between information and data; Data can be viewed as signs or recorded observations, which for some reason are not used, but only stored. Consequently, B. this moment time they do not affect the behavior, making decisions. However, the data is transformed into information if such an impact exists.

For example, the main array of data for the computer consists of such signs that do not affect the behavior. So far, this data is not organized accordingly and are not reflected in the form of an output result, so that the manager acts in accordance with them, they are not information. They remain data as long as the employee did not apply to them in connection with the implementation of certain actions or in connection with some solution he is obliged to accept.

The data is transformed into information when their value is realized. You can also say that in the case when it is possible to use data to reduce uncertainty about anything, the data is transformed into information.

Data life cycles

Like a substance and energy, the data can be collected, process, store, change their forms. However, they have some features. First of all, the data can be created and disappeared. For example, data on some extinct animal may disappear when a piece of coal is burned with its prints. Data can be wound up, lose accuracy, etc. Data can be characterized by a life cycle (Fig. 1.9), in which three aspects have the main value - birth, processing, storage and search.

Playback and data use can be carried out at various moments of their life cycle and therefore are not shown in the diagram.

Fig. 1.9. Life cycle of data

When processing on computer, the data is transformed, conditionally passing the following steps:

1) Data as a result of measurements and observations:

2) data on material information carriers (tables, protocols, reference books);

3) models (structures) of data in the form of charts, graphs, functions;

4) data in the data description language;

5) databases on machine media.

Data model

The data model is the kernel of any database. The emergence of this term in the early 70s of the twentieth century is associated with the works of American cybernetics E.F. The code, which reflected the mathematical aspect of a data model used in the sense of data structure. In connection with the needs of the development of data processing technology in the theory of automated information banks (ABI), in the second half of the 70s, the instrumental aspect of the data model appeared, the restrictions imposed on the data structures and operations with them were included in the content of this term.

In modern interpretation data modelit is defined as a set of rules for generating data structures in databases, operations over them, as well as restrictions of integrity, which determines the permissible links and data values, sequences of their change.

Thus, the data model represents many data structures, integrity constraints and data manipulation operations. Based on this, you can formulate the following working definition: the data model is a combination of data structures and processing operations.

Currently distinguished "three main types of data models: hierarchical, network and relational. Hierarchical data modelorganizes the data in the form of a tree structure and is the implementation of logical relationships: the children's relationship or relationship "integer - part". For example, the structure of the highest educational institution is a multi-level hierarchy (see Fig. 1.10).

Fig. 1.10. An example of a hierarchical structure

The hierarchical (tree) database consists of an ordered set of trees; More precisely, from an ordered set of several instances of one type of wood. In this model, the initial elements generate other elements, and these elements in turn generate the following elements. Each generated element has only one generating element. Organizational structures, lists of materials, table of contents in books, project plans, schedule of encounters and many other sets of data can be presented in hierarchical form.

The main disadvantage of this model is: a) the complexity of the connection between the objects of the "Many to many" objects; b) the need to use the hierarchy that was based on the database when designing. The need for constant data reorganization (and often the impossibility of this reorganization) led to the creation of a more general model - network.

The network approach to the organization of data is an expansion of a hierarchical approach. This model differs from hierarchical in that each generated element can have more than one generating element. An example of a network data model is shown in Figure 1.11.

Since the network database can represent directly all types of links inherent in this corresponding organization, according to these data, it is possible to move, explore and request them with all sorts of ways, i.e. The network model is not connected with only one hierarchy. However, in order to make a request for a network database, it is necessary to perfectly deep into its structure (have a diagram of this database at hand) and develop your database navigation mechanism, which is a significant disadvantage of this DB model.

Fig. 1.11. An example of a network structure

One of the shortcomings of the data models discussed above is that in some cases, with a hierarchical and network representation, the database growth may lead to a violation of the logical representation of the data. Such situations occur when new users appear, new applications and types of requests, when taking into account other logical links between data elements. These shortcomings avoids a relational data model.

The relational is considered to be such a database in which all data are presented for the user in the form of rectangular data values, and all operations on the database are reduced to manipulations with tables.

The table consists of columns (fields) and strings (entries); It has a name unique inside the database. The table reflects the type of object of the real world (essence), and each of its string is a specific object. Thus, the Sports Section table contains information about all children engaged in this - export section, and its lines are a set of attribute values \u200b\u200bof each particular child. Each table column is a set of values \u200b\u200bof a specific object attribute. Column weight, for example, is a combination of all weight categories of children engaged in the section. In a column, the floor may contain only two different meanings: "Husband." And "Women." These values \u200b\u200bare selected from the set of all possible values \u200b\u200bof the object attribute, which is called a domain. So, the values \u200b\u200bin the column weight are selected from the set of all possible scales of children.

Each column has a name that is usually written at the top of the table. These columns are called fieldstables. When designing tables within a specific DBMS, it is possible to choose for each field it a type,those. To determine for it a set of rules for its display, as well as identify those operations that can be performed on the data stored in this field. Type sets may vary from different DBMS.

The field name must be unique in the table, but different tables may have fields with the same name. Any table must have at least one field; Fields are located in the table in accordance with the procedure for following their names when it is created. Unlike fields, the strings are not names; The order of their follows in the table is not defined, and the number is logically unlimited. Rows are called notestables.

Since the lines in the table are not ordered, it is impossible to choose a string in its position - among them there is no "first", "second", "last". Any table has one or more columns, the values \u200b\u200bin which are uniquely identified each line. Such a column (or a combination of columns) is called the primary key. In the Table Sports section, the primary key is a column of F.O.O. (Fig. 1.12).

Such a choice of the primary key has a significant disadvantage: it is impossible to write to the section of two children with the same value of the field of F.O., which is not so rare in practice. That is why, often introduce an artificial field for numbering entries in the table. Such a field, for example, may be a magazine number for each child, which will be able to ensure the uniqueness of each entry in the table. If Tab.Litsa satisfies this requirement, it is called relation(Relation).

Fig. 1.12. Relational data model

Relational data models can usually support four types of ties between tables:

1) One to one(Example: In the same table, information about schoolchildren is stored, in another information about the passing by schoolchildren vaccinations).

2) One to many(Example: In the same table, information about teachers is stored, in another information about schoolchildren who have these teachers are class teachers).

3) Many to one(As an example, you can offer the previous case, considering it on the other hand, namely from the table in which information about schoolchildren is stored).

4) A lot to many(Example: In the same table, orders for the supply of goods are stored, and in another - firms that perform these orders, and several firms may be combined to perform one order.

Relational presentation of data has a number of advantages. It is understandable to a user who is not a specialist in the field of programming makes it easy to add new descriptions of objects and their characteristics, has great flexibility when processing requests.

Questions and tasks

1. Give the definition of the concept of "data".

2. What is the name of the data life cycle?

3. What data modes do you know?

4. Specify the advantages and disadvantages of each data model.

Information processes