5 areas covered by large data analysis techniques

Due to the particularity of large data, large data analysis technology is still in the development stage, old technology will be perfected, new technology will appear more.

1. Visual analysis

Data visualization is the most basic function for both ordinary users and data analysis experts. The data image can let the data oneself speak, lets the user intuitively feel the result.

2. Data Mining algorithm

Visualization is the translation of machine language to people, and data mining is the mother tongue of the machine. segmentation, clustering, isolated point analysis and a wide variety of algorithms to refine our data and value. These algorithms must be able to cope with the volume of large data, but also have a high processing speed.

3. Predictive analysis capacity

Data mining allows the analyst to digest the data-bearer information faster and better, thereby improving the accuracy of the judgment, and predictive analysis allows the analyst to make some forward-looking judgments based on the results of image analysis and data mining.

4. Semantic engine

The diversity of unstructured data poses new challenges to data analysis, and we need a tool system to analyze and refine data. The semantic engine needs to be designed to have enough artificial intelligence to be able to actively extract information from the data.

5. Data quality and data management

Data quality and management are the best practices for management, and processing of data through standardized processes and machines ensures that a predetermined quality analysis is achieved.

(Responsible editor: The good of the Legacy)