Wikipedia-differences between data warehouse, data mining, and OLAP

A data warehouse can be used as a data source for data mining, OLAP, and other analysis tools. Because the data stored in a data warehouse must be filtered and converted, the wrong data can be avoided, the result is incorrect.

Data Mining and OLAP are used as analysis tools. The difference is that OLAP provides users with a convenient multi-dimensional viewpoint and method to efficiently perform complex data query operations, the preset query conditions are pre-set by the user, while data mining can be used by the information system to actively explore hidden information that has not been found in the data source, and generate knowledge through user cognition.

Data mining is an automatic or semi-automated method for exploring and analyzing a large amount of data to create effective models and rules. Enterprises learn more about their customers through data mining, and then improve their marketing, business and customer service operations. Data mining is an important application of data warehouses. Basically, it is used to mine the hidden information in your data. Therefore, data mining is actually a part of the so-called knodge DGE discovery, data mining uses many statistical analysis and modeling methods to search for useful features (patterns) and relationships in data ). The knodge DGE discovery process has an important impact on the success or failure of data mining applications. Only in this way can data mining obtain meaningful results.