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How to avoid data redundancy? In view of this problem, this article introduces the corresponding analysis and answers in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible way.

Methods to avoid data redundancy: 1, repeated storage or transmission of data to prevent data loss; 2, data redundancy caused by simplifying the process; 3, storing the same data in different locations in order to speed up the processing process; 4, redundancy for other purposes.

Data redundancy refers to the duplication of data, or the phenomenon that the same data is stored in different data files. It can be said that increasing data independence and reducing data redundancy are prerequisites for the success of enterprise-wide information resource management and large-scale information systems.

Data redundancy is adopted in the application of data for some purpose.

1. Repeatedly store or transfer data to prevent data loss.

2. The redundant coding of the data can prevent the loss and error of the data, and provide the function of inverse transformation of the wrong data to get the original data.

3. The amount of data redundancy caused by simplifying the process. For example, send the same information to multiple purposes, store the same information in multiple locations, without analyzing the data and reduce the workload.

4. Store the same data in different locations in order to speed up the processing process. For example, different contents of the same information are processed in parallel, or the same information is processed in different ways.

5. In order to facilitate processing, the same information has different forms of expression in different places. For example, versions of a book in different languages.

6. The index of a large amount of data is often used in the database. Its purpose is similar to point 4.

7. Method information redundancy: for example, every driver has to remember the basic traffic information of the same city; a large number of personal computers are installed with similar operating systems or software.

8. Redundant data for completeness. For example, there are many words in the dictionary, but we only look up a few of them. The software has many functions, but we only use some of them.

9. Regular redundancy. Carried out in accordance with laws, systems, rules, etc. For example, there are a lot of stylized contents in the contract.

10. Redundancy for other purposes. For example, repeat information in order to be valued, and so on.

The answer to the question on how to avoid data redundancy is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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