关键词: 过程挖掘, 多粒子群协同, 启发式挖掘, 时序行为, 高斯变异

Abstract:

Stream data possesses realtime, continuous and sequential features. To detect implicit information and dynamic process in stream data, we propose a hybrid heuristic cooperative optimization algorithm based on time series prediction with selfadapting for stream process reengineering. Firstly, we define the stream process model, and improve the heuristic miner rules in the process logic based on the implicit dependency relation among log activities. Secondly, we define the ageing factor based on sequential behaviors and introduce the multiple particle swarm cooperation selfadapting strategy based on Gauss mutation to improve the local and global search capacity of the PSO algorithm, thus the process model is optimized and reengineered. Comparative experiments on four benchmark functions varify the better convergence and stability of the proposed algorithm in streaming process mining.

Key words: process mining, multiple particle swarm cooperation, heuristic miner, time series behavior, Gauss mutation