1, visual analysis

Big Data Analytics users have big data analytics experts, but there are also ordinary users, but both of them for the big data analysis of the most basic requirements is the visualization of the analysis, because the visual analysis of the characteristics of the big data to be able to intuitively present, and at the same time very easy to be accepted by the reader. It is as easy to accept as looking at a picture and talking.

2, data mining algorithms

The theoretical core of big data analysis is data mining algorithms, a variety of data mining algorithms based on different data types and formats in order to more scientifically present the characteristics of the data itself, and it is precisely because these are recognized by the world's statisticians, a variety of statistical methods (e.g., the use of data mining algorithms). It is also because of these statistical methods recognized by statisticians all over the world (which can be called the truth) that we can go deep into the data and dig out the recognized value. It is also because of these data-mining algorithms that big data can be processed more quickly; if an algorithm takes years to reach a conclusion, then the value of big data is lost.

3, predictive analytics

Big data analytics is ultimately one of the areas of application is predictive analytics, from the characteristics of the big data mining, through the establishment of a scientific model, and then can be modeled to bring in new data, so as to predict the future of the data.

4, semantic engine

The diversification of unstructured data has brought new challenges to data analysis, and we need a set of tools to systematically analyze and refine data. Semantic engines need to be designed with enough artificial intelligence to be sufficient to proactively extract information from data.

5. Data quality and data management

Big data analytics can't be separated from data quality and data management. High-quality data and effective data management, whether in academic research or in business applications, can ensure that analytics results are real and valuable.