A Low-Code Development Framework for Constructing Industrial Apps

  • Jingyue Wang
  • , Binhang Qi
  • , Wentao Zhang
  • , Hailong Sun*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationComputer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers
EditorsYuqing Sun, Dongning Liu, Hao Liao, Hongfei Fan, Liping Gao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages237-250
Number of pages14
ISBN (Print)9789811625398
DOIs
StatePublished - 2021
Event15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020 - Shenzhen, China
Duration: 7 Nov 20209 Nov 2020

Publication series

NameCommunications in Computer and Information Science
Volume1330 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020
Country/TerritoryChina
CityShenzhen
Period7/11/209/11/20

Keywords

  • BPMN
  • Crowd intelligence
  • Industrial app
  • Low code
  • Predictive maintenance

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