Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data).

Complex applications, which include microservices, computationally intensive batch jobs, and sophisticated interaction with the external environment, demand for heterogeneous computational infrastructures that range from cloud to HPC and edge computing.

16th International Conference on Open Source Systems

In the field of plant disease protection, many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications.

In general, one of the complexities of large simulations is related to the usage of the heterogeneous computational resources that are needed to execute them. The definition of workflows, usually linked to concrete orchestrations solutions, has reduced most of that complexity.

This paper presents an overview of the ECSEL 1 project entitled “MegaModelling at runtime – Scalable model-based framework for continuous development and runtime validation of complex systems” (MegaM@Rt2), whose aim is to address the challenges facing MDE.

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance.

Software Product Lines (SPLs) capture commonalities and variability of product families, typically represented by means of feature models.

Software Product Lines (SPLs) capture commonalities and variability of product families, typically represented by means of feature models.

Over the past decade, cloud services emerged as one of the most promising technologies in IT.

Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations.