CARTS: Constraint-based analytics from real-time system monitoring

Real-time applications are usually well-defined and operate based on a particular system model. However, in practical scenarios, the applications can perform differently because of the uncertainties in the environment. The system can use video streams to capture sequential real-time information of its surroundings. The system also needs to identify various constraints that have significant effects on its characteristics. Therefore, in this paper, we aim to monitor real-time applications and identify properties that can be used to analyze constraints such as performance or safety. By analyzing the constraints, we investigate the probability of failure as well as the performance of a system based on its behavior. An experimental analysis of a real-time traffic intersection monitoring demonstrates the applicability of our proposed approaches to extract useful information that can be used by a system to learn and adapt dynamic behaviors.