Snowflake Software will be demonstrating its XML/GML publishing tool, GO Publisher, for the first time at ESRI UC 2007 in San Diego later this month. The decision to participate at ESRI’s world-wide conference and exhibition is the result of Snowflake’s active participation in the EDN (ESRI Developer’s Network) programme and the accumulation of the development work Snowflake has been investing recently to support ArcSDE.
GO Publisher enables users to translate data and make it compliant to a given XML/GML schema (a Community Schema).
GO
Publisher is available in three versions: Desktop for single file
delivery, Agent for bulk file delivery and WFS for web services – a key
enabler to an off-the-shelf Spatial Data Infrastructure. Common across
the product line is the easy-to-use point and click GUI, removing any
need for programming or knowledge of complex scripting languages such
as XSLT.
Community schemas are becoming more important and widespread as the industry is facing the task of having to share data and provide ‘joined-up’ services. Schema are being developed on a regional level (IMRO - the Netherlands, PARSOL – UK), on an industry level (S100 for hydrographic data) and internationally for cross-border initiatives (INSPIRE). The ability for contributors to be able to supply their data according to these schemas has never been more critical.
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“Many of our customers are ESRI users and are storing their data in ArcSDE,” explains Ian Painter, Managing Director of Snowflake Software. “It's essential that they have the ability to extract this data and supply it according to one of the many schemas being demanded of them. GO Publisher's integration with ArcSDE opens up this opportunity with an easy-to-use and proven tool set.” CityGML generated by GO Publisher |
The demonstration at the Snowflake Booth (no 713) will illustrate how easy it is to extract data from ArcSDE and publish it to a given schema. The highlight of the demonstration will show 2D data stored in ArcSDE being delivered in 3D according to the CityGML schema. An additional demonstration will include a schema translating scenario where data from two disparate sources, in two different models are translated into a common Community Schema and published as a shared Web Feature Service.
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