The DFI can hold spatial or spatiotemporal data. It will really shine and outperform any other solution when working with data that has the following characteristics:

• It is spatiotemporal in nature, in other words, it models entities that move through space, over time

• There are high overall volumes of data that need to be indexed and queried, typically 10bn or more; or there is a need to ingest at a high throughput rate, i.e., 1m records or more per second

• The data ingested must be immediately queryable, with results available in real time

• Data needs to be queried with the well-known ‘points in polygon’ framework. This means that given an area of interest (a building, a geographical feature, a country’s borders) the users need to identify what entities were within it at some point in time. This query framework enables many use cases.

Here are some examples of datasets that may be well suited for DFI applications:

• Physical objects that move through space and record their position and readings

  • Personal devices such as mobile phones, tablets, laptops, and smart watches

  • Vehicle fleets (cars, trucks, bicycles, scooters, etc) as well as connected cars

  • Autonomous robots and drones: consumer (e.g. Roomba) and industrial (e.g. Delivery Bots)

  • Boats and planes

• Non-physical entities

  • Financial transactions

  • IP traffic

  • Tracking cyber security threats

• Stationary or slow-moving sensors that monitor physical objects

  • CCTV cameras with object detectors (e.g. automatic number plate recognition, footfall counters, etc)

  • Features extracted from aerial or satellite images

    
    

The above datasets could be combined to be able to search across them and surface spatiotemporal relationships (e.g. identify that a set of mobile devices are on the same ferry).