1. NVO Review -- San Diego -- 13-14 Jan 20051 The VO compared to Other O‘s Jim Gray Microsoft T HE US N ATIONAL V IRTUAL O BSERVATORY

2. NVO Review -- San Diego -- 13-14 Jan 20052 The Big Picture: online-science Data ingest Managing a petabyte Common schema How to organize it? How to reorganize it? How to coexist with others? Query and Visualization tools Support/training Performance –Execute queries in a minute –Batch (big) query scheduling The Big Problems Experiments & Instruments Simulations facts answers questions ? Literature Other Archives facts

3. NVO Review -- San Diego -- 13-14 Jan 20053 Eventually, they will all be integrated into a world-wide digital library There Are LOTS of Observatories High Energy Physics Mission To Planet Earth (EOS) Geology/Seismology Oceanography Hydrology Climate Agriculture Ecology / Biosphere Genomics/Proteomics/… Epidemiology/Medicine / … And…… Astronomy

4. NVO Review -- San Diego -- 13-14 Jan 20054 Astro VO compared to X VO Community –Started sooner (80’s) –All literature online –Have data sharing tradition –Smaller (1e4 professionals) –More cohesive –Astronomers know Math & Tech –No commercial/privacy issues Domain –Data is simpler –UCD common base vocabulary –Survey data volume is huge Systematic and well-documented –Good analysis and simulation at least by comparison. Community –Diverse groups do not see common issues –Building islands of information (stovepipes) –Need cyber-infrastructure but Have no funds to pay for it Horrified by price Disappointed in past investments –No charter for data curation & preservation Domain –New instruments imply new science methods –Forced to “objectify” the science –Current tools inadequate for data avalanche (from sensors and simulators) –Data avalanche requires New tools New algorithms (data mining) Data management and lineage (provenance) Spatial and temporal data is a central problem. Differences Similarities

5. NVO Review -- San Diego -- 13-14 Jan 20055 Personal Observations World-Wide Telescope (VO & NVO) is a model for other domains (widely cited). It is important to –Document successes and failures –Share tools and techniques –Benefit from other’s advances and tools –Pioneer problem areas Common data model / definitions Curation Preservation Tools (visualization, analysis, spatial, temporal, …