Research Article
Design of Distributed Multidimensional Big Data Classification System Based on Differential Equation
@INPROCEEDINGS{10.1007/978-3-030-67871-5_35, author={Pei-ying Wang}, title={Design of Distributed Multidimensional Big Data Classification System Based on Differential Equation}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part I}, proceedings_a={ADHIP}, year={2021}, month={2}, keywords={Differential equation Distributed multidimensional big data Classification system}, doi={10.1007/978-3-030-67871-5_35} }- Pei-ying Wang
Year: 2021
Design of Distributed Multidimensional Big Data Classification System Based on Differential Equation
ADHIP
Springer
DOI: 10.1007/978-3-030-67871-5_35
Abstract
In today’s more distributed and disorderly network environment, how to organize this information simply and effectively, so that users can quickly obtain potentially valuable data is a common problem in all fields. The commonly used classification systems are based on genetic algorithms and orthogonal decomposition. These two types of systems have high memory usage and low classification accuracy. Aiming at the above problems, a distributed multidimensional big data classification system based on differential equations is designed. The system design is mainly divided into three parts: the first design system overall framework; the second design system hardware, including multidimensional data integration module, central processing module, storage module, result output and display module; third, designing multidimensional big data according to differential equation Classification software main program. The results show that compared with the big data classification system based on genetic algorithm and the big data classification system based on orthogonal decomposition, the classification accuracy of distributed multidimensional big data classification system based on differential equation is improved by 8.75% and 6.75%, and the system memory occupancy rate is improved. Reduce by 35% and 12%.
