Multi-Backend Zonal Statistics Execution with Raven

Gereon Dusella, Haralampos Gavriilidis, Laert Nuhu, Volker Markl, Eleni Tzirita Zacharatou

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review


The recent explosion in the number and size of spatial remote sensing datasets from satellite missions creates new opportunities for data-driven approaches in domains such as climate change monitoring and disaster management. These approaches typically involve a feature engineering step that summarizes remote sensing pixel data located within zones of interest defined by another spatial dataset, an operation called zonal statistics. Although several spatial systems support zonal statistics operations, they differ significantly in terms of interfaces, architectures, and algorithms, making it hard for users to select the best system for a specific workload. To address this limitation, we propose Raven, a zonal statistics framework that provides users with a unified interface across multiple execution backends, while facilitating easy benchmarking and comparisons across systems. This demonstration showcases Raven 's multi-backend execution environment, domain-specific declarative language, optimization techniques, and benchmarking capabilities.
TitelCompanion of the 2024 International Conference on Management of Data
Antal sider4
ForlagAssociation for Computing Machinery
Publikationsdato9 jun. 2024
StatusUdgivet - 9 jun. 2024


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