Supported Data Models
These are the models we envisaged so far. For anything else, please get in touch.
We consider a sensor to be a device that is able to detect changes in an environment. A sensor is able to measure a physical phenomenon (like temperature, pressure, and so on) and transform it into an electric signal, the data, that is sent to this Data Hub.
Some common sensors that are widely adopted in everyday life - such as thermometers, pressure sensors, light sensors, accelerometers, gyroscopes, motion sensors, noise sensors and many more.
When adding a new sensor feed, we capture metadata that descibes what the data is (title and description), does it belong to a group/category, where is it, and what it measures (datastreams).
Events are something that happened that has a distinct start and end (time), duration, classification (from roadworks to football match or half term holiday), and happens in a certain geographical area.
Geographical information is crucial to help developers and application users to make sense out of vast information that arrives from sensors or events. Without knowing where are the bus stops and the order of bus route, real time location of a bus is not as useful. These datasets will have less frequent update schedule (monthly or annually) due to the fact that they will not change much through time (unless some external factor comes along).
We also expect that at some point one might have a sensor network - multiple sensors measuring same things on a set of different locations. To make it easier, we have created a notion of Collection.
Collection (of sensors) enables you to query all of your sensors at once - have a quick look through the Developers' API
to learn more.