Data Management Tools for Scientific Analytics

摘要

The scientific research landscape is changing. Scientists have the ability to generate data at an unprecedented scale and rate: lab techniques are becoming high-throughput; remote sensing deployments are more pervasive and use higher resolution sensors than ever before, and simulations on high-performance computing (HPC) platforms significantly expand the resolution of spatial and temporal events. As a result, science is becoming a data management problem. Hypotheses can now be tested by evaluating queries over massive datasets in secondary storage—in ferro experiments— rather than relying solely on in situ (field), in vitro (lab), and in silico (simulation) experiments as primary means of scientific discovery. This trend is further accelerated by the massive datasets that are collected, curated, and shared by entire communities of scientists (Boeckmann et al., 2003) [IRIS (Incorporated Research Institutions for Seismology: http:// www.iris.edu/), LSST (Large Synoptic Survey Telescope: http://www.lsst.org/), and SDSS (Sloan Digital Sky Survey: http://cas.sdss.org)].