Software systems managing big data have very challenging architectural requirements, e.g., in terms of performance, flexibility, and reliability. Big data systems often embody a number of technologies, such as cloud computing, the Internet of Things (IoT), fog computing, high-performance computing (HPC), artificial intelligence (AI), and in particular machine learning (ML). These technologies come with their own technical limitations and constraints that also impact the architecture of big data systems. Enforcing security, privacy, transparency, and ethics are also concerns of increasing importance, with potential architectural implications.
The Big Data Value Association (BDVA) initiated the SACBD workshop series to provide a forum for discussing all aspects of the software architecture challenges of big data systems. The workshop welcomes the contributions of practitioners and researchers alike. The workshop is open to all, both BDVA members and non-members.
List of Accepted Papers
- Measuring Performance Quality Scenarios in Big Data Analytics Applications: A DevOps and Domain-Specific Model Approach, Cristian Camilo Castellanos Rodriguez, Carlos A. Varela, Dario Correal
- Enhancing predictive maintenance using Fog Computing and Big Data paradigms, osu Díaz-de-Arcaya, Raúl Miñón, Ana Isabel Torre-Bastida
- Big Data from the Cloud to the Edge: the aggregate computing solution, Shaukat Ali, Ferruccio Damiani, Schahram Dustdar, Marialuisa Sanseverino, Mirko Viroli, Danny Weyns