The diversity of big data causes data to be divided into three data structures. What are they?

The diversity of big data causes data to be divided into three data structures, namely: 1. Structured data, which is data logically expressed and implemented by a two-dimensional table structure; 2. Unstructured data It is data whose data structure is irregular or incomplete, has no predefined data model, and is inconvenient to be represented by two-dimensional logical tables in the database; 3. Semi-structured data.

The operating environment of this tutorial: Windows 7 system, Dell G3 computer.

The diversity of big data causes data to be divided into three data structures: structured data, unstructured data and semi-structured data.

Structured data

Structured data is also called row data. It is data that is logically expressed and implemented by a two-dimensional table structure. Simply put, it is a database. Strictly follow data format and length specifications, and mainly store and manage them through relational databases.

Structured data markup is a way for websites to be displayed in search results in a better way. After structured data markup, the website can display rich web snippets well in search results.

Search engines all support standard structured data markup to provide users with a better online experience. Microdata tags in web pages can help search engines understand the information on web pages, making it easier for search engines to identify categories and determine relevance.

At the same time, structured microdata can allow search engines to provide richer search result summary displays, that is, detailed information that helps users with specific queries, allowing users to see important information about your products directly in the search results. . For example: the product's price, name, inventory status (whether the product is in stock), reviewer ratings and comments, etc. can all be seen directly in the search result summary.

These rich snippets help users understand whether the website is relevant to their search content, which can help get more clicks on the page.

For example, in the search results, more star ratings, number of reviews, prices and other factors are displayed, which undoubtedly increases the professionalism of the website and improves customers' trust in the website. Good exposure virtually increases the click-through rate and conversion rate of the website.

Unstructured data

The opposite of structured data is unstructured data that is not suitable to be represented by a two-dimensional database table.

Unstructured data is data whose data structure is irregular or incomplete, has no predefined data model, and is inconvenient to be represented by two-dimensional logical tables in the database.

Includes all formats of office documents, XML, HTML, various reports, pictures and audio, video information, etc. Databases that support unstructured data use multi-valued fields, single fields and variable-length field mechanisms to create and manage data items, and are widely used in full-text retrieval and various multimedia information processing fields

Semi-structure Data

Semi-structured data (semi-structured data). When designing an information system, data storage will definitely be involved. Generally, we will save system information in a designated relational database. We will classify the data by business, design corresponding tables, and then save the corresponding information to the corresponding tables. For example, if we build a business system and need to save basic employee information: job number, name, gender, date of birth, etc.; we will create a corresponding staff table.

But not all the information in the system can be corresponded to by fields in a table.

The semi-structured data model has a unique position in the database system:

(1) It is a data model suitable for database integration, that is to say , suitable for describing data contained in two or more databases that contain similar data in different schemas.

(2) It is a basic model of markup service for sharing information on the Web.

The structural pattern in semi-structured data is attached or blended with the data itself, and the data itself describes its corresponding structural pattern. Specifically, semi-structured data has the following characteristics:

(1) Data structure is self-describing. Structure and data merge, and there is no need to distinguish between "metadata" and "general data" in research and applications (the two become one).

(2) Complexity of data structure description. The structure is difficult to incorporate into various existing description frameworks, and it is difficult to clearly understand and grasp it in practical applications.

(3) Dynamic nature of data structure description. Data changes usually lead to structural model changes, and the overall structure model has a dynamic structure.

Conventional data models such as E-R model, relational model and object model are exactly the opposite of the above characteristics, so they can become structured data models. Compared with structured data, the composition of semi-structured data is more complex and uncertain, so it has higher flexibility and can adapt to a wider range of application needs.

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