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Feature

Geospatial data (R-tree)

RaimaDB supports an index algorithm designed specifically for geospatial data called an R-Tree. It is a balanced search tree that organizes its data into pages and is designed to group physically nearby objects and then represent that in the next level of the tree. This is the ideal index type when the users need quick retrieval of multi-dimensional data within a bounding box. A common use case for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc. 

Aerospace

Aerospace and defense

Autonomous flight
Live data capture
Post flight analysis
Flight planning
Capture lots of data
Reliably store data
Process data in real-time
Replicate data to the cloud
Securely communicate data
Low compute
High data volumes
Backlogged data not lost
Automotive

Automotive industry

Autonomous driving
Historical driving information
Infotainment & UX
Capture map data
Reliably store data
Process data in real-time
Use irregular information
Store alerted information
Merge sensor and map data
Triggers / Actionable
Industrial

Industrial automation

Live data capture
Smarter machines
Capture sensor data
Reliably store data
Process data in real-time
Talk to other devices
Light footprint
Low compute
Triggers / Actionable
Backlogged data not lost
Energy

Energy & natural resources

Live data capture
Smarter machines
Capture sensor data
Reliably store data
Process data in real-time
Use irregular information
Store alerted information
Low compute
Light footprint
Triggers / Actionable

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