Science Data Management: Maximizing the Yield

Big Data is characterized not only by the enormous volume or the velocity of its generation, but also by the heterogeneity, diversity, and complexity of the data. —Suzi Iacono, US Interagency Big Data Senior Steering Group W hile 2012 saw Big Data entering American business and popular culture,1,2 the phenomenon was definitively not news to the scientific community nor to CiSE readers. For years scientists have grappled with an exponential growth in data acquisition and generation, and 18 months ago the CiSE Big Data issue emphasized that although Big Data was creating “an extremely exciting time for scientific discovery,” many challenges remained before scientists could make optimal use of data-intensive scientific computing.3 Guest editors Francis J. Alexander, Adolfy Hoisie, and Alexander Szalay concluded that the sheer 1521-9615/13/$31.00 © 2013 ieee