We present preliminary work to construct a knowledge curation system to advance research in the study of regional economics. The proposed system exploits natural language processing (NLP) techniques to automatically implement business event extraction, provides a user-facing interface to assist human curators, and a feedback loop to improve the performance of the Information Extraction Model for the automated parts of the system. Progress to date has shown that we can improve standard NLP approaches for entity and relationship extraction through heuristic means and provide indexing of extracted relationships to aid curation.

We have witnessed a dramatic increase in national cyberinfrastructure resources to support data-driven research. Orchestrating these resources to enable the creation of collaborative infrastructure capable of supporting data intensive activities is challenging. In this work we present RADII, a novel architecture and system that enables the provisioning and configuration of collaborative infrastructure by orchestrating data and infrastructure management in an integrated manner. We also introduce a cross-layer data annotation mechanism that together with Software-Defined Networking (SDN) support allows the embedding of user-defined performance policies into file metadata which are translated into executable optimal network plans. We have deployed RADII on a worldwide production testbed and demonstrated through experimentation that RADII can improve network throughput of data transfers by 28.2% as compared to conventional approaches.

Genomic research involves a considerable amount of intensive computational and data management challenges including varying demand for computing resources, large data staging, management of complex workflows, and managing data and metadata across thousands of experiments and datasets. To address these challenges, researchers are typically forced to acquire and maintain experienced IT staff and informatics infrastructure. Increasingly researchers have explored cloud technologies, yet these lack key capabilities for data and workflow management. We introduce work to address these issues through use of federated cloud infrastructure coupled with data and workflow management technology. We present preliminary work toward the integration of three major technologies: ExoGENI, integrated Rule Oriented Data System (iRODS), and Pegasus/HTCondor, to develop a software infrastructure that better supports data-and workflow-centric genomic analysis.