TY - GEN
T1 - Bringing Cinco De Bio to the Cloud
AU - Brandon, Colm
AU - Mitwalli, Daniel Sami
AU - Krumrey, Marco
AU - Teumert, Sebastian
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents the successful migration of Cinco de Bio (CdB), a Low-Code / No-Code (LCNC) platform for health analysis workflows, from a hybrid desktop-server architecture to a fully cloud-native solution using Cinco Cloud. The migration addressed key limitations of the original architecture, including installation complexity, lack of real-time collaboration, and usability issues for non-programmers, the target user of CdB. The resulting cloud-based CdB platform now offers zero-install functionality, real-time multi-user collaboration, improved accessibility, and enhanced security features. The migration process highlighted the flexibility of LCNC platforms in evolving technological landscapes and demonstrated how architectural decisions in such platforms can significantly impact their long-term evolution and capabilities. By abstracting complex architectural decisions, CdB Cloud enables health researchers to focus solely on workflow design, exemplifying the principle of 'easy for the many, difficult for the few' in low-code development. This work contributes to the broader discussion on the role of software architecture in LCNC platforms and its impact on making sophisticated data analysis tools more accessible to domain experts in fields such as health research.
AB - This paper presents the successful migration of Cinco de Bio (CdB), a Low-Code / No-Code (LCNC) platform for health analysis workflows, from a hybrid desktop-server architecture to a fully cloud-native solution using Cinco Cloud. The migration addressed key limitations of the original architecture, including installation complexity, lack of real-time collaboration, and usability issues for non-programmers, the target user of CdB. The resulting cloud-based CdB platform now offers zero-install functionality, real-time multi-user collaboration, improved accessibility, and enhanced security features. The migration process highlighted the flexibility of LCNC platforms in evolving technological landscapes and demonstrated how architectural decisions in such platforms can significantly impact their long-term evolution and capabilities. By abstracting complex architectural decisions, CdB Cloud enables health researchers to focus solely on workflow design, exemplifying the principle of 'easy for the many, difficult for the few' in low-code development. This work contributes to the broader discussion on the role of software architecture in LCNC platforms and its impact on making sophisticated data analysis tools more accessible to domain experts in fields such as health research.
KW - Artificial Intelligence
KW - Health Informatics
KW - Low-code/No-Code
KW - Software Architectures
UR - https://www.scopus.com/pages/publications/105007937884
U2 - 10.1109/ICSA-C65153.2025.00055
DO - 10.1109/ICSA-C65153.2025.00055
M3 - Conference contribution
AN - SCOPUS:105007937884
T3 - Proceedings - 2025 IEEE 22nd International Conference on Software Architecture, ICSA-C 2025
SP - 340
EP - 349
BT - Proceedings - 2025 IEEE 22nd International Conference on Software Architecture, ICSA-C 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Software Architecture, ICSA-C 2025
Y2 - 31 March 2025 through 4 April 2025
ER -