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For many applications, it is important to evaluate trigger conditions on streaming time series. In a resource constrained environment, users' needs should ul- timately decide how the evaluation system balances the competing factors such as evaluation speed, result precision, and load shedding level. This paper presents a ba- sic framework for evaluation algorithms that takes user-specified quality requirements into consideration. Three optimization algorithms, each under a different set of user- defined probabilistic quality requirements, are provided in the framework: (1) minimize the response time given accuracy requirements and without load shedding; (2) min- imize the load shedding given a response time limit and accuracy requirements; and (3) minimize one type of accuracy errors given a response time limit and without load shedding. Experiments show that these optimization algorithms effectively achieve their optimization goals while satisfying the corresponding quality requirements.

Evaluating Trigger Conditions on Streaming Time Series with User-given Quality Requirements