STRATUM: A BigData-as-a-Service for Lifecycle Management of IoT Analytics Applications

Smart Internet of Things (IoT) applications require real-time and robust predictive analytics, which are based on Machine Learning (ML) models. Building ML models from Big Data is not only time-consuming, but developers often lack the needed expertise for feature engineering, parameter tuning, and model selection. The proliferation of ML libraries and frameworks, data ingestion tools, stream and batch processing engines, visualization techniques, and the range of available hardware platforms further exacerbates the system design, rapid development, and deployment problems. Finally, resource constraints of IoT require that the execution of the analytics engine be distributed across the cloud-edge spectrum. To overcome these daunting challenges, we present Stratum, which is an event-driven Big Data-as-a-Service offering for IoT analytics lifecycle management. Stratum provides users with an intuitive, declarative mechanism based on the principles of model-driven engineering to specify the application and infrastructure requirements. It automates the deployment via generative programming principles. This paper highlights the problems that Stratum resolves, demonstrating its capabilities using real-world case studies.