A Generic Machine Learning Model for Spatial Query Optimization based on Spatial Embeddings

Machine learning (ML) and deep learning (DL) techniques are increasingly applied to produce efficient query optimizers, in particular in regards to big data systems. The optimization of spatial operations is even more challenging due to the inherent complexity of such kind of operations, like spatial join or range query, and the peculiarities of spatial data. Although a few ML-based spatial query optimizers have been proposed in literature, their design limits their use, since each one is tailored for a specific collection of datasets, a specific operation, or a specific hardware setting. Changes to any of these will require building and training a completely new model which entails collecting a new very large training dataset to obtain a good model. This paper proposes a different approach which exploits the use of the novel notion of spatial embedding to overcome these limitations. In particular, a preliminary model is defined which captures the relevant features of spatial datasets, independently from the operation to be optimized and in an unsupervised manner. This model is trained with a large amount of both synthetic and real-world data, with the aim to produce meaningful spatial embeddings. The construction of an embedding model could be intended as a preliminary step for the optimization of many different spatial operations, so the cost of its building can be compensated during the subsequent construction of specific models. Indeed, for each considered spatial operation, a specific tailored model will be trained but by using spatial embeddings as input, so a very little amount of training data points is required for them. Three peculiar operations are considered as proof of concept in this paper: range query, self-join, and binary spatial join. Finally, a comparison with an alternative technique, known as transfer learning, is provided and the advantages of the proposed technique over it are highlighted.