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A Cost Model for Adaptive Resource Management in Data Stream Systems

Data stream management systems need to control their resources adaptively since stream characteristics as well as query workload vary over time. In this paper we investigate an approach to adaptive resource management for continuous sliding window queries that adjusts window sizes and time granularities to keep resource usage within bounds. In order to quantify the impact of both methods on a query plan, we develop an efficient cost model for estimating the resource allocation in terms of memory usage and processing costs. A thorough experimental study not only validates the accuracy of our cost model but also demonstrates the efficacy and scalability of the applied methods.