Structured Data Processing Method and Distributed Processing System Construction

Abstract

Today, data is gradually changing in popular culture in a continuous, rapid and time-varying direction. According to the new application framework, people put forward higher requirements for the performance and functions of data systems, and also put forward higher requirements for the practice and understanding of data processing. This paper aims to study the processing method of structured data and the construction of distributed processing system. This paper first analyzes the research status of distributed data systems at home and abroad, then designs a typical distributed data processing system and discusses two basic technologies of the system, and finally applies it to the project of postal centralized delivery system. This paper summarizes the structural and linguistic features of the text. According to these characteristics, this paper proposes an organizational structure. The method has three main parts: text prioritization, new text discovery and information extraction. This paper analyzes the characteristics and memory space allocation of continuous query, and designs a dynamic window query based on the greedy principle, which improves the efficiency of continuous query processing. Proven by experiment. The new word rate with a word length of more than 5 is 0%. In the case of different data volumes, the load change rate of the system is less than 0.05, and the system is stable.

Methods for aggregating multi‐source heterogeneous data in the IoT based on digital twin technology

“…Jasim N A believed that the lack of communication efficiency in the application of the IoT in the medical field is a very serious consequence, so data flow is very important. 15 Moreover, the data in IoT systems often contains a large number of high-dimensional features, which require in-depth data analysis and mining to extract useful information. The commonly used data analysis and mining methods include descriptive statistics, hypothesis testing, analysis of variance, regression analysis, cluster analysis, principal component analysis, decision tree analysis, neural network analysis, etc.…”

“…By converting and mapping data from different data sources, data consistency is enhanced, facilitating the flow of data between them. Jasim N A believed that the lack of communication efficiency in the application of the IoT in the medical field is a very serious consequence, so data flow is very important 15 …”