The form of the data includes, and what the data includes

Structured data: refers to relational model data, that is, data managed in the form of relational database tables, which is easier to understand when combined with typical scenarios, such as data in enterprise ERP, OA, and HR.

Unstructured data: refers to data with irregular or incomplete data structures, no predefined data model, and data that is inconvenient to be represented by two-dimensional logical tables in databases. Such as word, pdf, ppt and various formats of **, **, etc.

In fact, in addition to structured data and unstructured data, there is also a category of semi-structured data, so what is semi-structured data?

Semi-structured data: refers to data that is not relational and has a basic fixed structure pattern, such as log files, XML documents, JSON documents, e-mails, etc.

Extended question: How to deal with these three types of heterogeneous data?

1. For the integration of multiple structured data, the main focus is on the ETL processing and timeliness of data

The table structure is different, and different types of field mappings need to be done.

If you want to add a table field, you need to add a column.

If a table field needs to be reprocessed, it needs to be converted into a table, such as formulas or others.

When adding a table design, you need to ensure that there are three paradigms, which will not be mentioned here, but you can refer to: three paradigms of the database.

The timeliness of data synchronization, such as real-time synchronization, once every half an hour, or once a day, or whether real-time synchronization and fusion is required, should be determined based on specific business scenarios.

2. For semi-structured and unstructured data, because of the scattered data and the lack of unified management, it is necessary to borrow professional tools.

There are currently two ways to work with semi-structured, unstructured data:

Semi-structured (JSON, XML), Excel, and CSV are better to extract key information from semi-structured and unstructured data and reuse it in structured data, because the structure of this kind of data is relatively uniform.

To extract key information from files such as word and pdf, if it is a single text, some tools on the market may be able to achieve it, but if it is a large volume, it may be necessary to customize some regular expressions through the program to extract key information. In general, because of the inconsistent format and the inconsistent purpose, the program is more customized.

In this case, the value of the data and the input-output ratio will be comprehensively considered, because this part of the data processing is more complicated.

If you want to improve the input-output ratio, you can use professional tools such as finedatalink to support the fusion and integration of structured and semi-structured data, which is oriented to ETL data processing scenarios, and can also make data orchestration simpler and improve the use value of data.

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Data includes numerical data, such as various statistical or measurement data. Numeric data is discrete values within an interval; Simulation data, which consists of continuous functions, refers to the physical quantities that change continuously in a certain interval, and can be divided into graphic data (such as points, lines, and surfaces), symbol data, text data, and image data.

What the data includes.

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Data includes numerical data, such as various statistical or measurement data. The data of the number of source words is a discrete value in a certain interval; Modulus simulation data, composed of continuous functions, is a physical quantity that changes continuously in a certain interval in the code state, and can be divided into graphic data (such as points, lines, and surfaces), symbol data, text data, and image data.

Creating a suitable chart and bold in the worksheet helps to analyze and compare the data intuitively and vividly, and makes it easier for Bizhen to understand the theme and point of view, and through the setting of information such as ( ) and ( ) in the data in the chart, the focus of the problem can be effectively conveyed to the reader.

The following is a related extension, I hope it will be helpful to you: data is the result of facts or observations, a logical summary of objective things, and an unprocessed raw material used to represent objective things. Data is the manifestation and carrier of information, which can be symbols, words, numbers, voices, images, etc.

Data and information are inseparable, data is the expression of information, and information is the connotation of data. Data itself has no meaning, and data only becomes information when it has an impact on the behavior of entities. Data dryness can be continuous values, such as sounds and images, called analog data.

It can also be discrete, such as symbols, words, and is called numerical data. In computer systems, data is represented in the form of binary information units. In computer science, data refers to the general term of all the symbols that can be input into the computer and processed by the computer program, and is a general term for numbers, letters, symbols and analog quantities with a certain meaning that are used to input into the computer for processing.

It is the most basic element of the composition of a geographic information system, and there are many types.

Summary. Since the birth of database technology, there are three main ways to generate data.

1) Passively generate data.

Database technology makes the storage and management of data simple, the data generated by the business system during the operation can be directly saved to the database, and the data is generated with the operation of the business system, so the data generated at this stage is passive.

2) Proactively generate data.

The birth of the Internet of Things has greatly accelerated the development of mobile Internet and the probability of data generation. For example, people can generate data anytime and anywhere through mobile terminals such as mobile phones. Not only has user data increased proliferated, but users have also actively submitted their own behaviors, such as real-time sending**, emails, and other messages, bringing them into the era of social mobility.

The emergence of a large number of mobile terminal devices enables users not only to actively submit their own behaviors, but also to interact with their social circles in real time, so a large amount of data is generated and has extremely strong dissemination. Obviously, the data thus generated is proactive.

3) Perceptually generated data.

The development of the Internet of Things has revolutionized the way data is generated. For example, data acquisition devices such as cameras are located in all corners of the city, and data is continuously collected and generated.

What are the ways in which data is generated.

Since the birth of database technology, there are three main ways to generate data. (1) The passive generation of data database technology makes the storage and management of data simple, the data generated by the business system during the operation can be directly saved to the database, and the data is generated with the operation of the business system, so the data generated at this stage is passive. (2) The birth of the active generation of data Internet of Things has greatly accelerated the probability of data generation with the development of mobile Internet.

For example, people can generate data anytime and anywhere through mobile terminals such as mobile phones. Not only has user data increased proliferated, but users have also actively submitted their own behaviors, such as real-time sending**, emails, and other messages, bringing them into the era of social mobility. The emergence of a large number of mobile terminal devices enables users not only to actively submit their own behaviors, but also to interact with their social circles in real time, so a large amount of data is generated and has extremely strong dissemination.

Obviously, the data thus generated is proactive. (3) The development of perceptual data generation Internet of Things has completely changed the way data is generated. For example, data acquisition devices such as cameras are located in all corners of the city, and data is continuously collected and generated.

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There are three main ways to represent data: lists, graphs, and equations.

Law. The matters that should be paid attention to in its application and expression are described here.

Data Representation. There are three main ways to represent data: listic, graphic, and equation. The matters that should be paid attention to in its application and expression are described here.

Chinese name. Data Representation.

Foreign name. data expression

Formula. y=mx+b

Method. Graphing method, list method.

Related disciplines. Mathematics.

List method. After the experiment, the large amount of data obtained should be expressed in a list as neatly and regularly as possible, so that all the data can be clear at a glance, easy to process, calculate, easy to check and reduce errors. The following points should be noted when making a list:

1) Each table should have a concise and complete name;

2) In the first column of each row or column of the table, write the name and unit in detail;

3) The data in the table should be expressed in the simplest form, the power of the common.

The factor should be indicated under the name in the first column;

Data Representation. 4) In each row, the numbers should be arranged neatly, and the number of digits and decimal points should be aligned; (5) The original data can be listed in a table with the results of processing, and the processing methods and operation formulas are indicated under the table.

Graphing. There are many advantages to using the graphical representation of experimental results: first, it can directly show the characteristics of the data, such as maximum, minimal, turning point, etc.; Secondly, it is possible to use the graph as a tangent.

Find the area, and the data can be further processed. Graphing is used in a wide range of applications, the most important of which are:

1) Find the interpolated value. According to the data obtained from the experiment, the relationship curve between the functions is made, and then the physical quantities corresponding to a certain function are found.

value. For example, in the determination of heat of dissolution, the heat of dissolution of the salt can be directly found out according to the integrated heat of dissolution curves at different concentrations.

Data Representation. 2) Find extrapolated values. In some cases, the linear relationship between the measured data can be extrapolated beyond the measurement range to find the limit value of a function, which is called extrapolation. For example, strong electrolytes.

The value of the molar conductivity of the infinitely diluted solution cannot be directly determined by the experiment, but the molar conductivity of the solution with a very dilute concentration can be directly determined, and then the graph is extrapolated to a concentration of 0, that is, the molar conductivity of the infinitely diluted solution is changed to the cherry tree rate (3) as a tangent line to find the microquotient of the function. The micro-quotient that calculates the function from the slope of the curve is often used in data processing. For example, using the curve of the integrated heat of dissolution as a tangent line to find the differential heat of dilution at a given concentration from its slope is a very good example of a nucleus cluster.

Summary. What are the forms of data transactions at present?

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Hello, dear, there are a few kinds.

1.In the case of direct transaction data, the two parties to the transaction make a detailed agreement on the content and method, sign a contract for the slip or data transaction, and one party delivers the goods, one party pays, and the transaction is completed.

2.The data exchange model, some data exchanges established by the first government, under the supervision of the first place, in a centralized place for data supply and demand transactions.

4.Membership account service model.

5.The transaction of data cloud services is modular and dispersive, in which the seller provides cloud services for data applications, and consumers purchase cloud services or systems to destroy them, and obtain data application value through service stimulation.

Access mode. 7.Data transactions based on data protection technologies.

8.A data platform for stakeholders and a consortium transaction model for data.

The pros and cons of various ways can be understood.

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The key to artificial intelligence.

Key technologies for artificial intelligence.

The key to artificial intelligence is three elements: algorithms, computing, and big data.

The key technology of artificial intelligence is machine learning, which is the study of how the Burning World computer simulates or realizes human learning behavior in order to obtain new knowledge or limb cleaning skills, and reorganize the existing knowledge structure to continuously improve its own performance.

In addition, there are key technologies such as knowledge graph, natural language processing, human-computer interaction, computer vision, biometrics, and VR.

The main types of data are as follows:

1. byte: 8 bits, the maximum amount of data stored is 255, and the data range is between -128 and 127.

2. short: 16 bits, the maximum data storage capacity is 65536, and the data range is -32768 32767.

3. int: 32 bits, the maximum data storage capacity is 2 to the 32nd power minus 1, and the data range is negative 2 to the 31st power to the positive 2 to the 31st power minus 1.

4. long: 64 bits, the maximum data storage capacity is 2 to the 64th power minus 1, and the data range is negative 2 to the 63rd power of positive 2 minus 1.

5. float: 32 bits, the data range is in, f or f must be added after the number when directly assigning.

6. double: 64 bits, the data range is in, you can add d or d or not add when assigning value.

7. boolean: only true and false values.

8. char: 16 bits, store unicode code, assign value with single quotation marks.

Element Definition:

A data element, also known as a data element, is a data unit that describes its definition, identity, representation, and allowable values with a set of attributes. Data elements can be understood as the basic unit of data, and a data model is formed into an overall structure by several related data elements in a certain order