Scientific Data Management in the Coming Decade
<|reference_start|>Scientific Data Management in the Coming Decade: 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.<|reference_end|>
@article{gray2005scientific,
    title={Scientific Data Management in the Coming Decade},
    author={Jim Gray, David T. Liu, Maria Nieto-Santisteban, Alexander S. Szalay,
  David DeWitt, Gerd Heber},
    journal={arXiv preprint arXiv:cs/0502008},
    year={2005},
    number={Microsoft Technical Report MSR-TR-2005-10},
    archivePrefix={arXiv},
    eprint={cs/0502008},
    primaryClass={cs.DB cs.CE}
}