关键词: 活动, 事件日志, 表示学习, 流程树, 向量表示学习算法

Abstract: In business process modeling and analysis tasks,activities are usually represented as discrete symbols,which may results in the loss of activity semantics.To tackle this problem,a novel methodology for representation learning for activities named Activity to Vector(Activity 2Vec) was proposed to represent activities in event logs as real-valued vectors in a distributed semantic space,which could provide the support for modern deep learning techniques in business process modeling and analysis tasks.To address the limited-volume and sparsity issue of event logs,the raw log was enhanced through simulation of multiple process models.Furthermore,a vector representations learning algorithm was developed based on negative sampling,which was capable of learning both activity vectors and temporal interval vectors from the enhanced event log.Experiments on MIMIC III dataset demonstrated that the proposed method outperformed traditional Word2Vec in discovering high-quality activity vectors.

Key words: temporal activity, event log, representation learning, process tree, vector representations learning algorithm