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MavHome project provides rich applications for addressing various issues associated with stream data processing. In this paper, we present our approach for building a data stream management system (DSMS) for the above smart home project. We further summarize our primitive solutions for continuous query processing, Quality of Service(QoS) management, and mapping a trigger mechanism to a stream processing system.

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Currently, stream data processing is an active area of research, which includes everything from algorithms and architectures for stream processing to modelling, and analysis of various components of a stream processing system. In this paper, we present an analysis of relational operators used for stream processing using queueing theory and study behaviors of streaming data in a query processing system. Our approach enables us to compute the fundamental performance metrics of relational operators -select, project, and join over data streams. Furthermore, this approach establishes a way to find the probability distribution functions of both the number of tuples and the waiting time of tuples in the system. Finally, we designed and implemented a number of experiments to validate the accuracy and effectiveness of our analysis.