  Predicting the execution time of queries is an important problem with
applications in scheduling, service level agreements and error detection.
During query planning, a cost is associated with the chosen execution plan and
used to rank competing plans. It would be convenient to use that cost to
predict execution time, but it has been claimed in the literature that this is
not possible. In this paper, we thoroughly investigate this claim considering
both linear and non-linear models. We find that the accuracy using more complex
models with only the optimizer cost is comparable to the reported accuracy in
the literature. The most accurate method in the literature is nearest-neighbour
regression which does not produce a model. The published results used a large
feature set to identify nearest neighbours. We show that it is possible to
achieve the same level of accuracy using only the cost to identify nearest
neighbours. Using a smaller feature set brings the advantages of reduced
overhead in terms of both storage space for the training data and the time to
produce a prediction.
