nvo review -- san diego -- 13-14 jan 20051 the vo compared to other o‘s jim gray microsoft t he us...
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NVO Review -- San Diego -- 13-14 Jan 2005 1
The VO compared to Other O‘s
Jim GrayMicrosoft
THE US NATIONAL VIRTUAL OBSERVATORY
NVO Review -- San Diego -- 13-14 Jan 2005 2
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
Simulationsfacts
facts
answers
questions
?Literature
Other Archives facts
facts
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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
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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.
DifferencesSimilarities
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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, …