Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Abstract

The purpose of this paper is to research on the design and analysis of low delay deterministic network based on data mining association. This paper studies and implements the algorithm of mining page association rules. A session recognition algorithm based on log reference page and request time is proposed by using the time probability relationship of continuous requests. This method improves the accuracy of log data preprocessing and page association rules mining. This paper studies and tests the efficiency, prefetch timing and cache organization of the two page association rules. The results show that in this prefetch scheme, the prefetch performance of the association rules of unordered pages is better than that of the association rules of ordered pages. The prefetch performance when cache hits is better than that when cache fails and the cache fails to hit has better performance. In the case of a certain size of cache space, reasonable organization of cache space can further improve the cache hit rate and reduce the network delay.

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

“…It submits the web big data to memory for calculation. In this way, Spark needs to write to the disk once, instead of constantly writing files on the disk, so it achieves better processing capability [9].…”