Database trading refers to a real-time, application-oriented database with high timeliness requirements. It only focuses on the latest database, also known as transaction database.
Generally speaking, database trading
consists of a file in which each record represents a transaction. Generally, a transaction contains a unique transaction ID and a list of items that make up the transaction (such as goods purchased in the store). Database transactions may have some additional tables associated with it, including other information about sales, such as transaction date, customer ID number, and seller ID Number, sales branch, etc.
If we want to dig deeper into data, in business operation, we can ask "which products are suitable for sale together?" this "shopping basket data analysis" enables us to bundle products into groups as a strategy to expand sales. For example, given the knowledge that printers and computers often sell together, you can provide customers who buy selected computers with a very expensive printer Discount sales, hope to sell more expensive printers. The conventional data retrieval system can't answer the above query. However, by identifying the commodities frequently sold together, the data mining system of transaction data can do it. Here we mainly study the statistical method of data mining in transactional database.
The difference between database trading
and analysis database
Transactional databases are mainly real-time, application-oriented databases with high requirements for timeliness of response, only focusing on the latest period of data. It is usually called transactional database. Analytical database is mainly used to analyze laws in a large number of data. Generally, the data stored in the database has a long time span, a large amount of data, and low requirements for real-time. It is used for product decision-making by querying and analyzing the trend of laws.