Speaker
Description
Interactive exploration of Gaia’s two billion row tables requires specialised visual analytics pipelines that precompute multiresolution representations and provide low-latency access on standard user hardware. Building on the Gaia Archive Visualisation Service (GAVS), we have extended this framework within SPACIOUS through a fully containerised, cloud-deployable architecture and a redesigned data-production pipeline operating natively on Parquet. The refactored workflow improves memory efficiency, removes legacy format dependencies (gbin), and enables automated generation of visualisation products. A major enhancement is support for arbitrary user-selected attribute combinations, replacing the fixed histogram and scatter-plot variable sets available in the current GAVS. This required updates to preprocessing, indexing, and metadata handling to maintain fast retrieval across the full parameter space of Gaia tables. We also provide notebook integration (pyGAVS), allowing direct interaction with the visualisation server from Jupyter environments and supporting reproducible, and shareable workflows. With these developments, SPACIOUS provides flexible, scalable, high-performance visual analytics services for Gaia data releases and other large tabular datasets.
