Big data driven cyber physical systems not only meet big data 4V feature requirements, but also have to meet time constrains and spatial constraints of cyber physical systems. Big data driven cyber physical systems have to deal with time-constrained data and time-constrained transactions. They are now being used for several applications such as automobile and intelligent transportation systems, aerospace systems, medical devices and health care systems in each of big data driven cyber physical applications, data about the target environment must be continuously collected from the physical world and processed in a timely manner to generate real-time responses. Those systems contain a large network of sensors distributed across different components, which leads to a tremendous amount of measurement data available to system operators. Regarding big data modeling, an important question is how to represent a moving object. In contrast to static objects, moving objects are difficult to represent and model. The efficiency of modeling methods for moving objects is highly affected by the chosen method to represent and analyze the continuous nature of the moving object. The design of big data driven cyber physical systems requires the introduction of new concepts to model classical data structures, 4V features, time constraints and spatial constraints, and the dynamic continuous behavior of the physical world. In this paper, we propose a model based approach to model big data driven cyber physical systems based on integration of Modelica, Modelicaml, AADL, RCC and clock theory, we illustrate our approach by specifying and modeling Vehicular Ad hoc Networks (VANET).
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