I apply existing technology for the construction of monitoring systems to the ICU data set provided by the Symposium organizers. The large volume of data in the ICU set compels any reasoning system to operate efficiently. It is also a good test of scalability of the approach. In order to reach the required efficiency, heavy use must be made of abstraction in order to limit the extent to which new data samples cause reevaluation of derived conclusions. I describe the Temporal Control System (TCS), programming system designed for building intelligent temporal monitoring programs. Empirical results from the ICU date set validate the scalable design of the TCS. I then focus on the problem of generating interval values from sample points via persistence assumptions. The TCS provides both the framework for the implementation as well as a method of calculating the "cost" of different approaches. In particular, I show that limiting the time span of a persistent interval can be very costly and then suggest how the application of symbolic abstraction can help. Further performance improvements come from the development of additional temporal abstraction techniques.