Computer systems are full of heuristic rules which drive the decisions they make. These rules of thumb are designed to work well on average, but ignore specific information about the available context, and are thus sub-optimal. The emerging field of machine learning for systems attempts to learn decision rules with machine learning algorithms. In the database community, many recent proposals have been made to improve selectivity estimation with batch machine learning methods. Such methods are all batch methods which require retraining and cannot handle concept drift, such as workload changes and schema modifications. We present online machine learning as an alternative approach. Online models learn on the fly and do not require storing data, they are more lightweight than batch models, and finally may adapt to concept drift. As an experiment, we teach models to improve the selectivity estimates made by PostgreSQL's cost model. Our experiments make the case that simple online models are able to compete with a recently proposed deep learning method.

中文翻译：

---

通过在线机器学习进行选择性校正

计算机系统充满了启发式规则，这些规则驱动着它们做出的决定。这些经验法则旨在平均运行良好，但忽略了有关可用上下文的特定信息，因此是次优的。系统机器学习的新兴领域试图用机器学习算法学习决策规则。在数据库社区中，最近提出了许多建议，以使用批处理机器学习方法改进选择性估计。此类方法都是批处理方法，需要重新训练，无法处理概念漂移，例如工作负载更改和模式修改。我们将在线机器学习作为一种替代方法。在线模型即时学习，不需要存储数据，它们比批处理模型更轻量级，最终可能会适应概念漂移。作为一项实验，我们教授模型以提高 PostgreSQL 成本模型所做的选择性估计。我们的实验证明，简单的在线模型能够与最近提出的深度学习方法竞争。