Spatial Data Infrastructure as an Applications Platform

An essential function of an SDI is to provide an overall framework and architecture within which new applications can be developed and integrated. That does not necessarily mean that the SDI would itself need to encompass all those applications. Rather, the SDI could serve as a platform to support a large community (both in and outside USGS) to develop and operate a rich set of application services. In some cases, specific USGS elements or teams may need to build new applications to address specific science questions or meet specific mission needs. Using the SDI as a community applications platform would allow users to take advantage of existing applications that perform functions that they need rather than having to develop their own applications. In turn, when users develop new or improved services, they could more easily make them available to others through the SDI. By opening up the SDI to application developers in other federal agencies or in the much larger geospatial data community, this could yield substantial benefits in shared resources, reduction in duplication of effort, greater innovation, and expanded capabilities.

Spatial Data Infrastructure as a Workflow Platform

Recent work in other fields of electronic science, or e-science, demonstrates the valuable concept of high-throughput workflow processes as a means of processing and analyzing large and complex data resources (Taylor et al., 2007). Some workflow methods are designed to perform routine jobs and utilize the necessary computational protocols to undertake data-centric science. They enable scientists to focus on scientific discovery rather than having to spend effort and resources in routine data-processing. They also permit the development of more sophisticated tools for monitoring and detection (such as alert services for unusual or extreme events). Workflow approaches offer the opportunity to facilitate cross-disciplinary transfer and application of data-processing and analytic techniques in support of interdisciplinary research and problem-solving. Workflows that have been developed to address a specific problem in one field of science often are directly applicable to other, seemingly unrelated fields (Goble et al., 2008). Such cross-disciplinary uses of workflows can help to generate new analytic approaches, improve data quality and timeliness, reduce analysis costs, and speed the transfer of scientific knowledge to applications. Another important benefit of workflow approaches is the detailed record of data transformation and data-processing that is generated, which is a vital part of tracking the provenance of data and is critical for the long-term curation and reuse of data. Documenting and curating the workflows themselves may be an important role for an SDI in capturing geospatial expertise and ensuring the long-term reusability of the spatial data supported by the SDI.

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