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Editor to share with you big data's diversity so that the data is divided into how many data structures, I hope you will learn something after reading this article, let's discuss it together!

Due to the diversity of big data, the data is divided into three kinds of data structures, namely: 1, structured data, which is logically expressed and realized by two-dimensional table structure; 2, unstructured data is irregular or incomplete data structure, there is no predefined data model, it is not convenient to use database two-dimensional logical tables to represent data; 3, semi-structured data.

Due to the diversity of big data, data is divided into three kinds of data structures, namely: structured data, unstructured data and semi-structured data.

Structured data

Structured data, also known as row data, is logically expressed and implemented by a two-dimensional table structure, which is simply a database. Strictly follow the data format and length specifications, mainly through the relational database for storage and management.

Structured data markup is a way for websites to be displayed in search results in a better manner. With structured data markup, the site can well display rich web page summaries in search results.

Search engines support standard structured data tags to provide users with a better Internet experience. Microdata tags in web pages can help search engines understand the information on web pages, and it is more convenient for search engines to identify classification and judge relevance.

At the same time, structured micro-data allows search engines to provide a richer summary of search results, that is, to provide detailed information to help users with specific queries, so that users can see the important information of your products directly in the search results. For example, the price, name, inventory status (whether the product is in stock or not), reviewers' scores and comments can be directly seen in the summary of the search results.

These rich web summaries can help users understand whether the site is relevant to their search content and get more clicks on the page.

For example, some of the search results show more star ratings, number of comments, price and other factors, which undoubtedly increases the professionalism of the website and increases the trust of customers to the website. the good exposure of the website virtually improves the click rate and conversion rate of the website.

Unstructured data

In contrast to structured data, unstructured data is not suitable to be represented by two-dimensional tables in the database.

Unstructured data is a kind of data whose data structure is irregular or incomplete, there is no predefined data model, and it is not convenient to use database two-dimensional logical table to represent the data.

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

Semi-structured data

Semi-structured data (semi-structured data). When designing an information system, it will definitely involve data storage. Generally speaking, we will save the system information in a specified relational database. We will classify the data by business, design the corresponding table, and then save the corresponding information to the corresponding table. For example, we do a business system, to save the basic information of employees: job number, name, gender, date of birth and so on; we will establish a corresponding staff table.

But not all the information in the system can be simply matched with the fields in a table.

Semi-structured data (semi-structured data) model has a unique position in database systems:

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

(2) it is a basic model of marking service, which is used to share information on Web.

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

(1) the data structure is self-descriptive. With the integration of structure and data, there is no need to distinguish between "metadata" and "general data" in research and application.

(2) the complexity of data structure description. The structure is difficult to be incorporated into the existing description framework, and it is difficult to understand and grasp it clearly in practical application.

(3) the dynamic nature of data structure description. The change of data usually leads to the change of structural pattern, which has dynamic structural pattern as a whole.

Conventional data models such as Emurr model, relational model and object model are just the opposite of the above characteristics, so they can be 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 wide range of application requirements.

After reading this article, I believe you have a certain understanding of "how many data structures are divided into due to the diversity of big data". If you want to know more about it, welcome to follow the industry information channel. Thank you for reading!

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