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GML in the Back Office

 While a great deal has been written about the applicability or not of GML for consumer facing applications, I think the key role of GML for back office data aggregation and integration has been largely ignored. 

What are the requirements for back office data aggregation, and how might they differ from consumer facing applications?  Let’s take a look.

When we look at the problem of data aggregation we realize a number of things that those in the front office don’t have to be so concerned about.  Data aggregation by definition means getting data from a lot of different data sources, and that of course means having to deal with:

  • Multiple different coordinate reference systems.
    Many different data schemas (models) even where we are dealing with the same general application area (e.g. roads, streets, highways).  Extensibility is a must.
  • A range of geometry models – roads are seen differently by engineers than by mappers.
  • Presentation is not the key focus – data is.
  • Binding of metadata to the data for quality assurance and mapping to the application.

When we look at this side of the coin we see that GML has a lot to offer.  In particular:

  • GML can support more or less any coordinate reference system.
  • GML is feature centric and users can create schemas to match their requirements.  Simple or rich schemas can be created as needed.  No more complex that one needs, but no less expressive either.
  • GML offers a rich set of geometric primitives from simple points and lines to full 3D solids.  While not every application needs this – many do – and key data providers for many aggregation problems (think construction of 3D cityscapes) will require the full geometry capability.
  • While GML does provide a means of expressing styling rules, it is not loaded with presentation mechanisms – leaving this to other languages like KML , SVG or XAML.
  • GML provides a built in and completely extensible mechanism for handling metadata at the feature or feature collection level.

So while one might argue (I would) about the applicability of GML in consumer facing applications – there is no question about its application in the Back Office for data aggregation and data sharing – and without that happening – where will consumer facing applications get their data?

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