As science has become increasingly data-driven, and as data volumes and velocity are increasing, scientific advance in many areas will only be feasible if critical `big-data' problems are addressed - and even more importantly, software tools embedding these solutions are readily available to the scientists. Particularly, the major challenge being faced by current data-intensive scientific research efforts is that while the dataset sizes continue to grow rapidly, neither among network bandwidths, memory capacity of parallel machines, memory access speeds, and disk bandwidths are increasing at the same rate. Building on top of recent research at Ohio State University, which includes work on automatic data virtualization, indexing methods for scientific data, and a novel bit-vectors based sampling method, the goal of this project is to fully develop, disseminate, deploy, and support robust software elements addressing challenges in data transfers and analysis. The prototypes that have been already developed at Ohio State are being extended into two robust software elements: an extention of GridFTP (Grid Partial-File Transport Protocol)that allows users to specify a subset of the file to be transferred, avoiding unnecessary transfer of the entire file; and Parallel Readers for NetCDF and HDF5 for Paraview and VTK, data subsetting and sampling tools for NetCDF and HDF5 that perform data selection and sampling at the I/O level, and in parallel. This project impacts a number of scientific areas, i.e., any area that involves big (and growing) dataset sizes and need for data transfers and/or visualization. This project also contributes to computer science research in `big data', including scientific (array-based) databases, and visualization. Another contribution will be towards preparation of the broad science and engineering research community for big data handling and analytics.
