A Storage Model with Fine-Grained In-Storage Query Processing for Spatio-Temporal Data

Massive spatio-temporal data are continuously generated by various moving objects. To process these data for applications such as traffic forecasting, existing spatio-temporal systems all employ the move-data-to-computation paradigm. However, this approach suffers from significant data movement overhead between hosts and drives. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on a real hardware board. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to 81 % for widely used spatio-temporal queries compared to host computing solutions.