Radix: Enabling High-Throughput Georeferencing for Phenotype Monitoring over Voluminous Observational Data

Remote sensing of plant traits and their environment facilitates non-invasive, high-throughput monitoring of the plant's physiological characteristics. Effective ingestion of these sensing data into a storage subsystem while georeferencing phenotyping setups is key to providing timely access to scientists and modelers. In this study, we propose RADIX, a high-throughput distributed data ingestion framework with support for fine-grained georeferencing. Our methodology includes a novel spatial indexing scheme, the nested hash grid, for fine-grained georeferencing of data while conserving memory footprints and ensuring acceptable latency. We include empirical evaluations performed on a commodity machine cluster with up to 1TB of data. Our benchmarks demonstrate the efficacy of our approach.