TY - GEN
T1 - A Low-Code Development Framework for Constructing Industrial Apps
AU - Wang, Jingyue
AU - Qi, Binhang
AU - Zhang, Wentao
AU - Sun, Hailong
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - With the advent of the Industry 4.0, intelligent manufacturing has become a technological highland to conquer in the process of enterprise digitalization. As the core competitiveness of intelligent manufacturing, industrial apps, with new features such as customization and lightweight, has emerged as a new type of industrial software. Traditional development methods and tools can hardly meet the large demand of industrial software on account of its long development cycles while low-code development can greatly improve the productivity of industrial software, lower the barriers and reduce costs for development. Therefore, the research and application of low-code development for industrial apps has received much attention. Industrial Internet platforms such as Siemens, OutSystems have successively launched low-code tools. However, there is still a lack of an open, unified low-code development framework in industry. In response to the above problems, we propose a low-code framework to develop industrial apps quickly and easily, which paves the way for leveraging the crowd intelligence of worldwide developers to improve the productivity of developing industrial apps. Based on BPMN2.0 and Apache Activiti engine, this framework provides drag-and-drop process design, one-click process deployment and operation, data monitoring and other functions. In this paper, we present a prototype system of a low-code development framework and demonstrate its functions through a use case of developing a predictive maintenance application. Finally, the aircraft turbine life data is used to verify the effectiveness of the system.
AB - With the advent of the Industry 4.0, intelligent manufacturing has become a technological highland to conquer in the process of enterprise digitalization. As the core competitiveness of intelligent manufacturing, industrial apps, with new features such as customization and lightweight, has emerged as a new type of industrial software. Traditional development methods and tools can hardly meet the large demand of industrial software on account of its long development cycles while low-code development can greatly improve the productivity of industrial software, lower the barriers and reduce costs for development. Therefore, the research and application of low-code development for industrial apps has received much attention. Industrial Internet platforms such as Siemens, OutSystems have successively launched low-code tools. However, there is still a lack of an open, unified low-code development framework in industry. In response to the above problems, we propose a low-code framework to develop industrial apps quickly and easily, which paves the way for leveraging the crowd intelligence of worldwide developers to improve the productivity of developing industrial apps. Based on BPMN2.0 and Apache Activiti engine, this framework provides drag-and-drop process design, one-click process deployment and operation, data monitoring and other functions. In this paper, we present a prototype system of a low-code development framework and demonstrate its functions through a use case of developing a predictive maintenance application. Finally, the aircraft turbine life data is used to verify the effectiveness of the system.
KW - BPMN
KW - Crowd intelligence
KW - Industrial app
KW - Low code
KW - Predictive maintenance
UR - https://www.scopus.com/pages/publications/85111168344
U2 - 10.1007/978-981-16-2540-4_18
DO - 10.1007/978-981-16-2540-4_18
M3 - 会议稿件
AN - SCOPUS:85111168344
SN - 9789811625398
T3 - Communications in Computer and Information Science
SP - 237
EP - 250
BT - Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers
A2 - Sun, Yuqing
A2 - Liu, Dongning
A2 - Liao, Hao
A2 - Fan, Hongfei
A2 - Gao, Liping
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020
Y2 - 7 November 2020 through 9 November 2020
ER -