Efficient optimization and processing for distributed monitoring and control applications

In recent years, we have seen an increasing number of applications in networking, sensor networks, cloud computing, and environmental monitoring, that aim to monitor, control, and make decisions over large volumes of dynamic data. In my dissertation, we aim to enable a generic framework for these distributed monitoring and control applications, and address the limitations of prior work such as data stream management systems and adaptive query processing systems. In particular, we make the following contributions: 1) supporting the maintenance of recursive queries over distributed data streams, 2) enabling full-fledged cost-based incremental query re-optimization, and 3) as ongoing work, incorporating the cost estimation of plan switching during query re-optimization. Our solutions are implemented and evaluated using our prototype system Aspen, over a variety of workloads and benchmarks. In addition, our prototype system Aspen enables an end-to-end framework to support control and decision-making over integrated data streams from both the physical world (e.g., sensor streams) and the digital world (e.g., web, streams, databases).