The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data

Despite the success of these learned indices in improving the performance Learned indices have been proposed to replace classic index structures of some types of queries, they still have various limitations, like B-Tree with machine learning (ML) models. They require e.g., they can only handle spatial point objects and limited types to replace both the indices and query processing algorithms currently of spatial queries, some only return approximate query results, deployed by the databases, and such a radical departure is and they either cannot handle updates or need a periodic rebuild likely to encounter challenges and obstacles. In contrast, we propose to retain high query efficiency (Detailed discussions are in Section a fundamentally different way of using ML techniques to 2). These limitations, together with the requirement that the improve on the query performance of the classic R-Tree without learned indices need a replacement of the index structures and the need of changing its structure or query processing algorithms.