The human life in the twenty-first century is surrounded by technology. From household to transportation, education to hobbies, and safety to sports, information technology plays a major role in daily activities. Social interaction, education and health are just a few examples of areas where the rapid evolution of technology has had a major positive impact on the quality of life. Companies are increasingly relying on embedded systems to increase productivity, efficiency and business value. In factories, the precision of robots tends to replace human versatility. Connected devices such as drones, autonomous vehicles, smart watches or smart houses have become increasingly popular in recent years, offering a variety of facilities with high productivity. However, it must be guaranteed that their integration will not have repercussions on the human life nor on other objects or equipment of various interests. For this to be possible, the functioning of such systems must be predictable over time through the application of temporal constraints which must be verified to that end; hence real time systems. From this perspective, one of the specificities of real-time systems is that they must respond timely to events in their environment. Basically, these systems are composed of a large number of applications (tasks, programs) that are continuously communicating by propagating input data from one to the other. The communicating applications are organized into producers and consumers. Usually, communication between producers and consumers is done through communication registers or buffers. Producer applications use these communication channels to write output data while consumers use them to retrieve the required input data. A sequence of applications involved in the definition of a specific function is called a functional chain. The data is propagated through the functional chain and the delay between the time the data is generated at the beginning of the functional chain and its consumption at its end is deemed to be bounded. Associating a time delay to such data makes them real time data. A system of applications can be composed of several functional chains where some of the chains can propagate real-time data coming from different subsystems or triggered by different clocks. Additionally, a functional chain may be composed of applications executing at different rates, resulting in under- or over-sampling of the data. Evolving technologies within real-time embedded systems make these systems intelligent in the sense that, at a given time, they are mandated to perform targeted functions autonomously. For example, autonomous vehicles have the ability to sense the surrounding environment and navigate by themselves while making driving decisions. This autonomy in decision making raises a particular interest on the data sharing management system in between applications since the correctness of the decisions highly depends on the quality of used input data. Therefore, for such intelligent systems, it will only be sufficient that all the tasks can be scheduled to be sure that the overall functioning of the system is correct. It should be noted that task scheduling consists in assigning to each application CPU time units necessary to complete its execution, an execution order when executed with other tasks in accordance with a scheduling policy that considers these priorities. The schedulability of the task system is confirmed if all the tasks manage to complete their executions before their deadlines. Finally, the overall functioning correctness depends both on the system schedulability and the quality of the input data, which is translated into a set of properties associated to these data.