关键词: 延迟敏感流, 流挖掘系统, 挖掘算法, 能量效率

Abstract: In order to improve the mining accuracy and energy efficiency of delay-sensitive data, a dynamic scheduling latency-sensitive stream network mining algorithm is proposed. First, the algorithm establishes stream mining system model, and analyses choose probability, energy consumption and latency-sensitive of classifier chain. Then, in order to control the delay time mining system and save energy, constraint equation based on energy minimization combination delay is proposed. At the same time, it uses an effective decomposition bound algorithm to solve the optimal processing speed of the classifier selection problem, and finally finds a combination of minimum energy equation boundary by greedy algorithm. Stream mining system maintains at a higher efficiency with the classification low energy consumption and latency. Simulation results show that compared with algorithm based on dynamic time warping algorithm and data mining algorithm based on genetic algorithm optimization of data, energy efficiency is increased by 39.4% and 41.4%, classification accuracy is increased by 11.5% and 5.9%, respectively, with better energy efficiency and excavation accuracy.

Key words: delay-sensitive traffic, stream mining system, mining algorithms, energy efficiency