In an effort to maximize data uniformity and usability, the United States Department of Defense (DoD) mandated each component of the United Sates military conform to the Spatial Data Standards for Facilities, Infrastructure, and Environment (SDSFIE) data model. Each DoD component was given the opportunity to develop an adaptation of SDSFIE; an approved version of the data model tailored towards a given DoD component while retaining core attribution.
The United States Navy developed an adaptation of the SDSFIE model to be implemented via the GeoReadiness Program, a business line created to serve as a geospatial data warehouse for the Naval Facilities Engineering Command (NAVFAC) and the Commander, Navy Installations Command (CNIC). Data maintained by the GeoReadiness Program is leveraged as the backbone for the GeoReadiness Explorer (GRX), iNFADS, and other products across the Navy enterprise.
The GeoReadiness Program was utilizing the SDSFIE 2.6 data model as its data standard, however a new version of the model, SDSFIE 3.0, was released. To maintain compliance, all data needed to be migrated into the new standard once an adaptation was developed. GISi has storied relationship with the Navy's GeoReadiness Program and was selected to execute a solution to migrate the Navy's data. A migration of this magnitude had not been attempted within the DoD landscape leaving GISi to break new ground in developing standard operating procedures for big data.
A thorough review of the data revealed that an out of the box solution would not be viable for the client. Specialized processes would need to be established to handle attribute and feature class mapping in addition to data projection. A front-end MS-Access application was constructed to crosswalk 2.6 data into the 3.0 schema. This allowed subject matter experts across the Navy enterprise to identify the best match for their data and designate which attributes would be maintained in a tabular format. ETLs (extract, transform, load) were then built (within ArcGIS for Desktop) using the crosswalk data, each tailored at the regional level. Due to the intricacies of the crosswalking and data projection needs, it was not feasible to build or run these in a batch process. In the end, over a thousand ETLs were utilized. On average over 100 feature classes from each installation were migrated.