Groundhog: Accelerating Spatio-Temporal Data Analytics With Fine-Grained In-Storage Processing

With the rapid growth of mobile devices and applications, a prodigious number of spatio-temporal data are generated constantly. To process these data for applications like traffic forecasting, existing spatio-temporal systems rely on the move-data-to-computation paradigm. However, this approach incurs significant data movement overhead between hosts and storage devices, particularly when a spatio-temporal query is executed on a non-preferred data layout or when the query has a small result size due to its inherent nature. 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 real hardware and demonstrated how to apply fine-grained techniques to accelerate various spatio-temporal queries. 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.