| 摘要 |
A learning-based method for estimating costs or statistics of an operator in a continuous query includes a cost estimation model learning procedure and a model applying procedure. The model learning procedure builds a cost estimation model from training data, and the applying procedure uses the model to estimate the cost associated with a given query. The learning procedure uses a feature extractor, a confidence adjustor and a cost estimator. The feature extractor collects relevant training data and obtains feature values. The extracted feature values are associated with costs and used to create the cost estimator. The extracted feature values, the associated costs, the cost estimator, and a user interface are used to create a confidence adjuster. When applying the confidence adjuster and the cost estimator to a continuous stream of data, the feature extractor extracts feature values from the data stream, uses the extracted feature values as input into the confidence adjuster to determine whether or not the cost estimator should be used, and if so, uses the extracted feature values as inputs into the cost estimator to obtain the desired cost values.
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