Decentralized data processing leads to data redundancy

1. In terraria's world, minerals are indispensable for player development. Some minerals can be obtained from nature, some from falling. There are great differences between different grades of minerals, and there will be some differences between the same grade of minerals. The specific types of minerals depend on personality
there are five levels of natural minerals in front of the mountain, the worst is copper / tin, followed by iron / lead, then silver / tungsten, followed by gold / platinum, and the best is Yuyan in hell (at the bottom of the map), which is composed of Yuyan and obsidian (magma generated in water). The mineral after the slash is slightly better than that before the slash. Iron / lead
and gold / platinum
have many uses, please collect more
there are two kinds of unnatural minerals in front of the meat mountain, magic gold / blood
and meteorite, magic gold and blood have their own advantages and disadvantages, and exist in a very small amount in nature
the first three kinds of natural minerals are formed after destroying the demon altar, which are cobalt blue / palladium, Mithril / Shantong and refined gold / titanium; After the slash is slightly better than before. Yelvkuang is generated in the jungle after defeating the mechanical three queens. It will grow
Ye lvding + Xinghun synthesize the ghost ingot (mage, not in the picture), ye lvding + luminous mushroom synthesize the blue ingot (remote, not in the picture), and the total moon drops the luminous pearl to synthesize the luminous ingot
the above is all the content Xiaobian brings to you, and more wonderful strategies are in the 18183 teraria zone.

2.

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

data rendancy will hinder the integrity of the data in the database, and also cause a waste of storage space. One of the main goals of database design is to rece data rendancy as much as possible. One of the main ideas of the normalization theory of relational schema (hereinafter referred to as NF theory) is the principle of minimum rendancy, that is, the normalized relational schema should have the minimum rendancy in a sense

However, there is no standard concept available in NF theory. According to the principle of equivalence, there may be several definitions of rendancy with or without universal relation assumption

in the application of extended data

data rendancy is adopted for a certain purpose

1. Repeatedly store or transfer data to prevent data loss

2. Code the data rendantly to prevent the loss and error of the data, and provide the function of inverse transformation of the error data to get the original data

3. Data rendancy caused by simplifying the process

In order to speed up the processing process, the same data is stored in different places

In order to facilitate processing, the same information has different forms in different places

The index of large amount of data is often used in database

7. Information rendancy of method class

8. Rendant data for completeness

9. Regular rendancy. In accordance with the law, system, rules and other constraints

10. Rendancy for other purposes

3. When we analyze the data, we need to sort out the data, so as to facilitate the data analysis. Of course, data processing is the work before data analysis, and there are many data processing technologies in big data processing, the most common of which is rendancy elimination. So what is data rendancy? In this article, we will give you a detailed answer to this question
first of all, let's talk about data rendancy. In fact, data rendancy refers to the plication or excess of data, which is a common problem in many data sets. Data rendancy will undoubtedly increase transmission overhead, waste storage space, lead to data inconsistency and rece reliability. So many researches have proposed data rendancy rection mechanisms, such as rendancy detection and data compression. These methods can be used in different data sets and application environments to improve performance, but also bring some risks. For example, the data compression method brings additional computational burden in data compression and decompression, so it needs to make a trade-off between the benefits of rendancy rection and the increased burden. However, there is a large amount of data rendancy in the image and video data collected by widely deployed cameras. In the video surveillance data, a large number of images and video data have rendancy in time, space and statistics. Video compression technology is used to rece the rendancy of video data, and many important standards have been applied to rece the burden of storage and transmission
for ordinary data transmission and storage, this involves a technology, that is, data De plication technology, which is a special data compression technology, used to eliminate plicate data. In the process of storage de plication, a unique data block or data segment will be assigned an ID and stored, which will be added to an ID list. When the de plication process continues, a new data block whose ID already exists in the ID list will be considered as a rendant block. The data block is replaced by a reference to the stored data block pointer. In this way, only one instance of any given block exists. De plication technology can significantly rece the storage space, which plays a very important role in big data storage system
in the above content, we have introced a lot of data preprocessing methods. In fact, another method is to preprocess specific data objects, such as feature extraction technology, which plays an important role in multimedia search and DNS analysis. These data objects usually have high dimensional feature vectors. Data morphing technology is usually used to deal with heterogeneous data generated by distributed data sources, which is very useful to deal with commercial data
through this article, we can easily find that the data processing technology is very complex, but these technologies can better help us to eliminate data rendancy. Therefore, we must master the method of removing rendant data before removing rendant data. Only in this way can we lay a good foundation for the follow-up data analysis.

4.

Disadvantages of data rendancy:

1, waste of storage space

2, the efficiency of data interaction and database access is reced

But proper data rendancy can speed up the query. Whether the data rendancy is good or bad, we should make a reasonable choice according to our own projects

when the same data block is stored in two or more separate locations, data rendancy will occur. Suppose you create a database to store sales records and enter the customer address in each sales record. However, there are multiple sales to the same customer, so the same address is entered multiple times. The repeatedly entered address is rendant data

extended data

certain rendancy can improve performance

1, space for time

there is a dictionary table city with two fields of ID and cityname, and a business table with fields of ID, cityid, XXX, XXX, etc. If you want to query the business table, you must join the city dictionary table. If the business table is very large, the query will be very slow. At this time, we can use rendancy to solve this problem

directly replace the cityid in the business table with cityname, so we don't need to go to the dictionary table of the city of join when querying the business table. This approach is obviously not in line with our database design paradigm, but such rendancy may be necessary

2. Query a certain status value data

there is a field status in the business table to store submitted and uncommitted data. Suppose that uncommitted data in this table is less than submitted data. When users query all uncommitted data, they need to query all uncommitted data, and then filter out the uncommitted data. If this business table is very large, then the efficiency of such a query is very slow

at this time, we can rendance the uncommitted data in this business table into a new table, so that when users query uncommitted data, they can directly query in this uncommitted table, and the query speed is very fast

5. Not SQL server will have data rendancy, but all databases will have data rendancy. Most of the data rendancy is e to the incomplete consideration of the relationship between tables when building the database, which involves the concept of normal form in the database (currently the highest is NF normal form, but the general design is in line with the third normal form)
rendancy will lead to: 1. Waste of storage space. 2. The efficiency of data interaction and database access is reced.

6. Because the read-out data is any one of the rendant data, if there are too many rendant data, the probability of getting the same data twice is very small.

7. 1. The database will become larger and larger
2. The data query will be slow
3. There will be contradictions when fetching reference columns
4. The disk space will become larger and larger
5. The performance will be very poor when executing select *

to sum up, rendant data is good or bad, which depends on the function of the system. In general, it is better to have rendant columns in the business system.

8. You have a wrong understanding of the concept
related attributes refer to the influence relationship between attributes and output variables
attribute rendancy refers to the influence relationship between attributes
for example, forecasting house prices. The identified attributes are house area, geographical location, living room area and population quality
there is no interaction between population quality and housing price. We think that population quality is not related and needs to be removed
there is a strong correlation between house area and living room area. We think that one of these two attributes is rendant and needs to be removed.

9. Good building owner, I can give you an explanation:

data rendancy: data rendancy refers to an attribute stored in multiple tables, such as the employee's mobile phone number, which may exist in the salesperson's table or in the attendance table. This actually affects the integrity and consistency of the data. In other words, when there is an error in a table, it may cause problems in the query of the whole function
data insertion exception: it means that the inserted data violates the provisions of the database object, resulting in incorrect insertion of exception results. For example, there are three columns in a table that identify three attributes. If you write four attributes when inserting, the data will not be inserted. This is a simple insertion exception. Or if a field is required to be non empty and you insert a null value, it will also cause an insert error
data deletion exception: refers to the deletion error caused by the fact that data cannot be deleted when you need to delete it. For example, when there are foreign key restrictions in the table, there will be errors in deleting data
modification exception: refers to the error caused by the fact that the data cannot be updated when you update the data. For example, if you update a self incrementing column, the database will prompt that the update has failed. This is the modification exception
data exception: data exception refers to the existence of illogical data in the database, or the extraneous and unrelated data problems for no reason. For example, suppose the database has a proct table with price and an order table with sales quantity corresponding to the proct and an order amount table with order amount. According to the common logic, price * proct quantity = order amount, but if you find that there is data that does not conform to this logic, then there is a data exception. Data exceptions are usually related to system bugs or functional things written by the database. Sometimes, if the logic coverage is not strict, the data will be abnormal
the above is all the explanations of the landlord's questions, please adopt them

10. Reorganize the data table, optimize the relational database

hope your problem can be solved, hope to adopt!