2005/02/02 by Jim Gray, David T. Liu, Gray, Jim +11 · 3 citations
Computer Science · Decision Sciences · #Scientific Computing and Data Management #cs.CE #cs.DB
paper · pdf · doi:10.48550/arxiv.cs/0502008
arxiv created 2005/02/02 · arxiv updated 2009/12/01
This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.