Utilizing the grid computing technology of the TRIAD system for your data integration needs.
The field of informatics has been going through a rapid change over the past decade. New technologies, such as grid computing and knowledge anchored data, are combined with major funding and growing community thrusts designed to break down institutional boundaries in order to create a richer research and clinical environment. Funded NIH projects, such as NIH/NCI Cancer Bioinformatics Grid (caBIG), BIRN, and CTSA have lead to new ways to bring together data and services within and across institutional boundaries. Their common purpose is to increase the speed, efficiency, and outcomes of clinical and translational research efforts spanning the field of medicine.
The TRIAD system, which will be used as the middleware system enabling The Ohio State University (OSU) Center for Clinical and Translational Science (CCTS) to create a scalable, secure, and knowledge anchored data sharing environment, will adopt and adapt the caGrid infrastructure designed for the caBIG program.
The Informatics Research and Development (IR&D) group within the Department of Biomedical Informatics at OSU has been involved with these major community efforts in various capacities. For example, since the inception of caBIG, IR&D has been the lead architect and development site for caGrid - a middleware system designed for caBIG in order to create a loosely coupled, yet highly interoperable grid service oriented architecture (SOA). caGrid, in it’s most basic state, is a generic, domain agnostic software system comprised of grid middleware, tools, and services that can be leveraged to create an SOA that is secure, distributed, and semantically interoperable. The caBIG program uses caGrid as the backbone infrastructure of their cancer research grid infrastructure.
Within the OSU CCTS program we are faced with many of the same challenges as the caBIG community, including:
- Physically and logically disparate community participants.
- Multi-institutional security interoperability issues.
- Use of new technologies, which require training, expertise, and process change.
- Complex federal and local data integrity and privacy constraints.
- Semantic and syntactic differences in data within and across research groups and institution.