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It is challenging to support the timeliness of real-time data service requests in data-intensive real-time applications such as online auction or stock trading, while maintaining the freshness of temporal data that capture the current real-world status. Although deadline-aware real-time scheduling would significantly enhance the timeliness of data services, it is not clear how to assign explicit feasible deadlines to data service requests in an open environment. To address the problem, we design a new deadline assignment scheme to derive feasible deadlines for real-time data service requests considering their individual data needs. Further, we develop a systematic closed-loop approach to supporting the desired tardiness-the actual service delay to deadline ratio-of real-time data services even in the presence of dynamic workloads. We choose the tardiness metric due to its expressiveness compared to the deadline miss ratio and utilization that saturate at 0 and 1 when the system is underutilized or overloaded, respectively. The performance evaluation results acquired in our real-time stock trading testbed show that the desired average/transient tardiness is closely supported. Consequently, the deadline miss ratio is significantly reduced compared to a state-of-art database system with a real-time scheduling extension.