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Intro

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Altus Server Introduction

Altus Server is the most efficient way to generate streamable or offline content for the Altus Mapping Engine. It is comprised of a set of efficient programs that can run stand-alone on a single machine, in parallel on multiple cores of machines, or plugged in as part of your own back-end infrastructure. Altus Server can run on 32 and 64-bit OS X and several variants of Linux. Altus Server is designed to be compatible with both commercial GIS platforms like ArcGIS or with open platforms like the Geospatial Data Abstraction Library.

Altus Server components are designed specifically for the types of data they process: raster, point, vector, terrain, and weather.

Each component is designed to process and compress source data into streamable or packaged formats that work on the Altus Mapping Engine across all supported platforms.

Data assets produced by Altus Server are identical on all Altus Mapping Engine™ platforms. In the enterprise, all developers can access all mapping assets instantly and include them in their applications. Or mapping assets can be siloed on a department-by-department basis, with each department managing its own assets and server pipelines.

There is one server component for each map data type:

• Altus Terrain Processes terrain data for creation of terrain base layers.
• Altus Marker Processes static marker layers up to one million markers.
• Altus Vector Processes vector layers produced by MapShop.
• Altus Weather Processes various forms of weather data.
• Altus Package Converts assets produced by other server components into portable, optimized, files for offline use.

Supported Platforms

Altus Server components are generally portable to any platform with a C++ compiler and where required libraries are available. This includes Linux (CentOS, Ubuntu, etc.), OS X, and Windows. If you have a custom platform with gcc 4.7 or higher running on it, we can usually support it.

Parallel Data Pipelining

Several of the Altus Server components are designed in such a way that multiple instances of them can be run in parallel either on the same computer each running as a different process, or across a network, where many can be running on an array of servers on in a cloud infrastructure. How you deploy these in parallel is largely up to you and the kind of data you are trying to process.