A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

A broad spectrum of application domains are increasingly making use of heterogeneous and large volumes of data with varying degrees of humans in the loop. The recent success of Artificial Intelligence (AI) and, in particular, Machine Learning (ML) further amplifies the relevance of data in the development, maintenance, evolution, and execution management of systems built with model-driven engineering techniques. Applications include critical infrastructure areas such as intelligent transportation, smart energy management, public healthcare, and emergency and disaster management; many of these systems are considered socio-technical systems given the human, social, and organizational factors that must be considered during the system life-cycle [1]. This article introduces a conceptual reference framework – the Models and Data (MODA) framework – to support a data-centric and model-driven approach for the integration of heterogeneous models and their respective data for the entire life-cycle of socio-technical systems.