Name
Abstract | ||
|---|---|---|
A dynamic intelligence expression method is presented in this paper, which uses big data analysis to represent the intelligence to be taken from Web. In this method, reasoning methods are used to create new ideas which can be added to field intelligence systems in favor of big data analysis. This is used for the generalization of the well-known analysis to implement rule based generalization. The method plans to produce a learning model which best take offs the class members of a marked rule base. The object categories are given by an interface which is represented by the standards of a mathematical method. The category is defined by the formula. In our big data method, the learned artificial intelligence model is represented by models and it is consisted of a best condition of expressions of a given category. We show that this feature gives scholar choices to get ideas into the application field. Furthermore, the expression according to models adds additional value to the function and enables to answer questions, which big data function method cannot. The big data expression of the models can be explained by scholar. The reasoning logic can be added to the existing artificial intelligence expression method. Additionally, the reasoning logic obtaining method can be used repeatedly. In each procedure, new ideas from the search step can be added to the reasoning rule sets to enhance the comprehensive characteristics of the presented reasoning methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10586-017-1578-9 | Cluster Computing |
Keywords | DocType | Volume |
Big data analysis, Artificial intelligence, Information query, Feature detection, Generalization | Journal | 22 |
Issue | ISSN | Citations |
Supplement | 1573-7543 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruo Hu | 1 | 0 | 3.38 |
| Hui-min Zhao | 2 | 0 | 0.68 |
| Hong Xu | 3 | 0 | 1.35 |