Big data has the characteristics of large data volume, fast speed, complex types, different data structures and low value distribution density. Therefore, big data analysis has become a challenging task. At this time, some technologies need to be used to analyze big data in order to obtain a lot of intelligent, in-depth and valuable information. Common big data analysis technologies are as follows:
(1) Predictive analysis. This is also one of the use values of big data analysis. Through the analysis of existing data, predict the future data development trend and better provide predictive data for the development of the industry. Predictive analysis mainly mines the characteristics of data, establishes a scientific data model, introduces new data, and obtains new prediction results as a reference in the process of industry development.
(2) Data quality and data management. The quality of data and whether the analysis results of big data are consistent with the actual situation are important aspects of testing the results of big data analysis and determine whether the data is truly valuable. Whether high-quality data can be extracted requires effective data management.
(3) Visual analysis. Whether for data analysis experts or ordinary users, data visualization is the most basic requirement of data analysis tools. Visualization can display data intuitively, let the data speak for itself, and let the audience “hear” the results.
(4) Semantic engine. The diversity of unstructured data brings new challenges to data analysis, so a series of tools are needed to parse, extract and analyze data. The semantic engine needs to be designed to intelligently extract information from “documents”.
(5) Data mining algorithm. Big data has a large amount of data. Some simple algorithms or mathematical statistics are difficult to work. Data mining algorithms are needed to obtain the characteristics and value of the data. Clustering, segmentation, outlier analysis and other data mining algorithms can go deep into the data and mine the value of big data. These data mining algorithms must not only deal with the amount of big data, but also the speed of big data.