The basis and content of relational database normalization theory

Normalization theory is of great significance to database design, which is mainly reflected in the following aspects:

1.Reduced data redundancy: Normalization theory can help us eliminate or minimize duplicate information in data tables, thus avoiding data redundancy and inconsistencies. This improves the efficiency, reliability, and maintainability of the database.

2.Abnormal reduction of data updates: Database snapshot design through normalization theory can make each table contain only one entity type, and each attribute is associated with that entity type.

This avoids inconsistencies with other records when a record is inserted, deleted, or modified.

3.Query efficiency improvement: Database design through normalization theory can make query statements simpler and clearer, and use indexes and other technologies to optimize query efficiency.

At the same time, it can also effectively reduce the number of join operations when querying multiple tables.

4.Clear data structure: Database design through normalization theory can make the data structure clearer and easier to expand and maintain. This facilitates later development work.

In conclusion, normalization theory is a very important database design principle, which has great value in ensuring data integrity, correctness and stability.

The normalization theory of relational databases is a very important concept and occupies an important position in database design. The main purpose of normalization theory is to solve the problems of redundant data, data dependence and data inconsistency in the database, so as to improve the data quality and efficiency of the database, and make it more stable and reliable. Specifically, normalization theory mainly solves the following three problems.

Data redundancy. In a database, data redundancy refers to the repeated occurrence of the same data in multiple places. If there is too much data redundancy, it will lead to a waste of data storage space, and it will also bring great trouble to data maintenance and update.

By splitting different data into multiple tables, the normalization theory reduces data redundancy and improves the data storage efficiency and data update speed of the database.

Data dependency.

Data dependency is when the value of one data item depends on the value of another data item. In databases, the presence of data dependencies can lead to inconsistencies and redundancy in the data. Normalization theory makes the data in a database more consistent by splitting disparate data into multiple tables, thereby eliminating data dependencies.

Data inconsistencies.

Data inconsistencies are when the same data has different values in different tables. This situation can lead to inaccurate and unreliable data. Normalization theory avoids the problem of data inconsistency by splitting different data into multiple tables, so as to make the storage of data more standardized and the update and maintenance of data more convenient.

In conclusion, normalization theory is a very important part of relational database design, and its main purpose is to solve the problems of redundant data, data dependency and data inconsistency in the database, so as to improve the data quality and efficiency of the database. In the process of database design, it is necessary to follow the principles of normalization theory and reasonably design the database structure, so as to make the database more reliable, efficient and easy to maintain.

The main problem that the normalization theory of relational databases solves is (a).

a. How to construct a reasonable logical structure of the database.

b. How to construct a reasonable application interface for the database.

c. How to construct a reasonable physical structure of the database.

d. How to control the operation permissions of different users.

Relational Databases:

A relational database is a database based on the relational database model, which processes the data in the database with the help of concepts and methods such as set algebra.

A relational database is also a formally descriptive set of data items that can be accessed (or re-assembled) in different ways without having to reorganize the database.

Types and values of relational databases:

The type of relational database is called a relational database pattern, which is a description of a relational database, the definition of a number of domains, and a number of relational patterns defined on those domains. The value of a relational database is a collection of relationships that correspond to these relational patterns at a certain point in time, often referred to simply as a relational database.

Database Historical Research:

In 1970, Dr. Edgar Frank Codd (or E F Codd), a researcher at IBM and known as the "father of relational databases", published a report entitled "A Relational Model of Data for Large Shared Data Banks" in the journal Communication of the ACM.

This paper puts forward the concept of relational model of database for the first time, and lays a theoretical foundation for relational model.

At the end of the 70s of the 20th century, the theoretical research of relational methods and the development of software systems have achieved great results, and the relationship database experimental system System R developed by IBM San Jose Laboratory on IBM 370 series machines has been successful for 6 years. In 1981, IBM announced the launch of a new database product, SQL DS, with all the characteristics of System R.

Because the relational model is simple and clear, and has a solid mathematical theoretical foundation, it has been highly valued and widely responded to by academia and industry, and has quickly become the mainstream of the database market. Since the 80s of the 20th century, almost all of the data management systems launched by computer manufacturers have supported relational models, and most of the current research work in the field of databases is based on relational models.

Answer]: Normalization in database design is a work carried out in the logical design stage, which is responsible for standardizing the relational pattern to reduce redundancy, and to a certain extent eliminate modification exceptions, insertion exceptions and deletion exceptions.

Answer]: One of the tasks in the logical design stage is to further normalize the relationship pattern. Because the generation of the relationship pattern does not fully meet the requirements, there will be data redundancy and update exceptions, which requires the decomposition of the relationship pattern according to the normalization theory to eliminate redundancy and update exceptions.

Sometimes, however, depending on the number of processing requirements, some redundancy may need to be added to meet the processing requirements. In the logic design stage, the task needs to be partially processed in the form of a relational modular core, decomposing, merging or adding redundant attributes to improve storage efficiency and processing efficiency.

Answer]: B normalization theory provides us with theoretical standards for judging the advantages and disadvantages of relationship patterns, helps us to solve problems that may arise, and provides algorithms for automatically generating various patterns, so it is a powerful tool for designers, and also makes the design work of the database in the number of Hu has a strict theoretical basis. Although the normalization theory was originally proposed for the design of relational patterns, it is also of great significance for the design of the model database of the Guanjiao system, but also for the design of other model databases.

Answer]: The data dictionary is used to define the specific meaning of each component in the data flow diagram, and it provides a consistent definition and detailed description of the relevant elements for system analysis, design and maintenance in an accurate and non-ambiguous description method. Database design can be divided into requirements analysis stage, conceptual structure design, logical structure design, and physical structure design.

Requirements Analysis Phase: Analyze user requirements, including data, functionality, and performance requirements; Conceptual structure design: In the analysis stage, the system data flow diagram and data dictionary have been obtained, and now it is necessary to combine the data normalization theory to clearly represent the user's data requirements with a data model.

The conceptual data model is a problem-oriented model that reflects the user's real-world working environment and is independent of the specific database implementation technology. The process of building a conceptual data model of a system is called conceptual structural design. Logical structure design:

According to the established conceptual data model and the characteristics of the data model of a database management system software, the conceptual model is converted into a logical data model that can be accepted by the database management system according to certain conversion rules. Different database management systems provide different logical data models, such as hierarchical models, mesh models, and relational models. Physical Structure Design:

The process of selecting the most suitable physical structure for a logical data model is called database physical structure design. The storage structure and access method of the database on a physical device are called the database physical data model.