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Service Enabled Geoprocessing - What's in it for the GEOINT Analyst?


Most GEOINT analysts are aware that the community is moving to implement app stores that contain the tools, models, and widgets needed to perform common analytical tasks. Two examples are the NGA GEOINT App Store  and the DIA Orion Program. Behind these storefronts in the Cloud are server side components like the ENVI Services Engine and Esri ArcGIS for Server that perform advanced geospatial imagery and data processing tasks.


So what does the analyst gain from this type of architecture?

I think the primary benefit is access. In a stovepiped desktop based environment, you may not have the tools you need. And if you do, it may be difficult to take them with you. By service enabling geoprocessing tasks, you can access the same capability available in your work area, from an operations room or a deployed location. The App Store concept focuses on providing mission relevant apps that have been proven and validated by other analysts.

That brings me to a second benefit – community. Application stores will be one method for connecting analysts from disparate mission areas. Analysts will have the opportunity to rate and comment on the performance of the apps at the storefront. This, along with messaging services like chat, will foster community and the exchange of tradecraft. I believe this community, aided by subject matter experts and systems engineers, will be responsible for producing apps that represent the gold standard for a given intelligence problem. Instead of using a generic desktop application, you get a tool that has been 'proven in battle'.

The last benefit I'll mention here is scalability. You might think of this a more of an IT or enterprise benefit, but scalability ensures that analysts get their results quickly. On the desktop, your options for batch processing are limited by the power of your stand alone system. In an online architecture, processing power is allocated based on the size of your processing request or the number of processing requests you make. For example, when you kick off a large image processing job in the Cloud multiple computing cores are used to satisfy the request. This method is faster and frees up your machine to work on submitting the next request.

I'm looking forward to working with analysts as the community moves to the online on demand environment. Do you agree with these benefits? What are some concerns we should be prepared to address?