Track Information

Emerging Computing Technologies and Trends for Business Process Management (ECT4BPM)

Business Process Management (BPM) allows controlling an organization’s processes. This discipline allows analyzing, modeling, monitoring, executing, and managing efficient and effective business processes. It aims to align the Business process activities with the need of the client and making the organization more flexible, automated, and powerful. However, different new challenges appear related, for example, to the complexity of a large-scale BPM and the adaptation of business operations to ensure customer growth and engagement.

There is a variety of new emerging computing technologies and trends (e.g., cloud/edge, IoT, and blockchain) that can be used and applied in the different steps of business process management (e.g., monitoring and executing). They allow to achieve new goals and help to make smarter decisions. This track examines the blend of these emerging trends and technologies with BPM in terms of issues undermining this blend, solutions achieving this blend, and recommendations sustaining this blend.

This track encourages high-quality research and industrial papers that describe contributions related to applying new and emerging technologies in business process management.

This track seeks to serve as an annual venue that will enable the discussion on the trends, challenges and developments of Blockchain technology. In doing so, it aims to speed up the study and accelerate the adoption of this technology.


For this track, contributions are devoted to new and emerging technologies and trends in business process management. Specifically, the relevant topics include, but are not limited to:

  • Blockchain for BPM
  • Cloud computing-based BPM
  • Fog / Edge computing-based BPM
  • Mobile technologies for BPM
  • Industry 4.0 and BPM
  • Smart cities and BPM
  • Robotic process automation and BPM
  • Internet-of-things for BPM
  • Artificial intelligence for BPM
  • Business intelligence and BPM
  • Data science applied to BPM

Track chairs

  • Slim Kallel, University of Sfax, Tunisia
  • Walid Gaaloul, Telecom SudParis, France
  • Mohamed Sellami, Telecom SudParis, France