Real-time applications such as e-commerce, flight control, chemical and nuclear control, and telecommunication are becoming increasingly sophisticated in their data needs, resulting in greater demands for real-time data services. Since the workload of real-time databases (RTDBs), providing real-time data services, cannot be precisely predicted, they can become overloaded and thereby cause temporal violations, resulting in a damage or even a catastrophe. Imprecise computation techniques address this problem and allow graceful degradation during overloads. In this paper, we present a framework consisting of a model for expressing QoS requirements in terms of data and transaction preciseness, an architecture based on feedback control scheduling, and a set of algorithms implementing different policies and behaviors. Our approach gives a robust and controlled behavior of RTDBs, even for transient overloads and with inaccurate run-time estimates of the transactions. Further, performance experiments show that the proposed algorithms outperform a set of baseline algorithms, including FCS-EDF that schedules the transactions using EDF and feedback control.