A hyper-box approach using relational databases for large scale machine learning

In this paper We follow a simple approach which allows the implementation of machine learning (ML for short) techniques to large data sets. More specifically, we study the case of on-demand dynamic creation of a local model in the neighborhood of a target datum instead of creating a global one on the whole training data set. This approach exploits the advanced data structures and algorithms, embedded in modern relational databases, to identify the neighborhood of a target datum, rapidly. Preliminary experimental results from a large scale classification problem (HIGGS dataset) show that the typical machine learning techniques are applicable to large data sets through this approach, under particular conditions. We highlight some restrictions of the method and some issues arising by implementing it.