The goal of the data intensive LDRD was to investigate the fundamental research issues underlying the application of High Performance Computing (HPC) resources to the challenges of data intensive computing. We explored these issues through four targeted case studies derived from growing LLNL programs: high speed text processing, massive semantic graph analysis, streaming image feature extraction, and processing of streaming sensor data. The ultimate goal of this analysis was to provide scalable data management algorithms to support the development of a predictive knowledge capability consistent with the direction of Aurora.