Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a
compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CLASS Project (www.class-project.eu), grant agreement No. 780622.Peer ReviewedPostprint (author's final draft
