Machine learning workflow recommendation for data analysis in industrial Internet
The characteristics such as multi-modality and strong association of Industrial big data have brought many challenges.How to effectively accomplish the data analysis process according to the requirements of industrial appli-cations is a complex,time-consuming and labor-intensive task.In view of this task,a method of machine learning workflow recommendation for data analysis was proposed in industrial Internet.It started from existing solutions and utilized their involved datasets and machine learning workflows to provide recommendation.Based on Doc2vec model and the maximum average difference method,the similarities between existing solutions and the data analysis re-quirements by their text descriptions and data distribution features were calculated,by which suitable machine learn-ing workflows in existing solutions could be selected and recommended.The result of simulation experiments showed effectiveness of the proposed method.
NETL
NSTL
万方数据
