A Scalable Data Processing Model for Big Data Analysis of Enterprise Technology Innovation

Abstract

In many areas such as enterprise technology innovation, the volume of data to be analyzed grows rapidly. In order to analyze and use these huge data resources, we must rely on effective data analysis technology. However, the traditional data processing technology has encountered certain obstacles in scalability. A Scalable Data Processing Model (SDPM) was proposed in this study to solve problems concerning innovative services of enterprises. The model performed formal analysis and description of SDPM, and through this model, it implemented the clustering analysis and incremental computing algorithm of enterprise technology innovation application data. Research results demonstrate the potential of the proposed model can effectively analyze enterprise technology innovation data, and have similar performance in incremental computing for different sizes of data sets. The query response time is less than 1 second on average. The research results of this study show that the SDPM can adapt to the service needs of enterprise technology innovation data and improve the efficiency of data processing. This proposed model provided technical and theoretical support for big data processing.