Query Size Estimation Using Machine Learning

In a previous paper [6], we introduced the notion of using machine learning techniques to solve the problem of query size estimation in database query optimisation. In this paper, we build on this work by describing a new generic algorithm to correct the training set of queries for our machine learning method in response to updates. The training set correction algorithm is not only useful in the context of our machine learning approach, but is also useful for improving existing query size estimation methods whose performance deteriorates in the presence of high update loads. A by-product of our correction algorithm is that training sets can be fixed-size, allowing the error-level to be set in advance. Experimental results show that our machine learning technique performs well (and better than alternative methods) after the correction algorithm is applied.