Data aggregation is extensively used in data management systems nowadays. Based on a data aggregation taxonomy named DAGGTAX, we propose an analytic process to evaluate the run-time platform and time-related parameters of Data Aggregation Processes (DAP) in a real-time system design, which can help designers to eliminate infeasible design decisions at early stage. The process for data aggregation design and analysis mainly includes the following outlined steps. Firstly, the user needs to specify the variation of the platform and DAP by figuring out the features of the system and time-related parameters respectively. Then, the user can choose one combination of the variations between the features of the platform and DAP, which forms the initial design of the system. Finally, apply the analytic method for feasibility analysis by schedulability analysis techniques. If there are no infeasibilities found in the process, then the design can be finished. Otherwise, the design must be altered from the run-time platform and DAP design stage, and the schedulability analysis will be applied again for the revised design until all the infeasibilities are fixed. In order to help designers to understand and describe the system and do feasibility analysis, we propose a new UML (Unified Modeling Language) profile that extends UML with concepts related to real-time data aggregation design. These extensions aim to accomplish the conceptual modeling of a real-time data aggregation. In addition, the transferring method based on UML profile to transfer the data aggregation design into a task model is proposed as well. In the end of the thesis, a case study, which applies the analytic process to analyze the architecture design of an environmental monitoring sensor network, is presented as a demonstration of our research.