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High-performance stream processing is critical in many sense-and-respond application domains—from environmental monitoring to algorithmic trading. In this paper, we focus on language and runtime support for improving the performance of sense-and-respond applications in processing data from high-rate live streams. The central tenets of this work are the programming model, the workload splitting mechanisms,...
Distributed stream processing systems (DSPSs) have many important applications such as sensor data analysis, network security, and business intelligence. Failure management is essential for DSPSs that often require highly-available system operations. In this paper, we explore a new predictive failure management approach that employs online failure prediction to achieve more efficient failure management...
High-volume source streams, coupled with fluctuating rates, necessitate adaptive load shedding in data stream processing. When ignored, a continual query (CQ) server may randomly drop items, when its capacity is inadequate to handle the arriving data, and degrade the quality of the query results. To alleviate this problem, filters can be used at the source nodes. However, regular source filtering...
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