Layered Frame Analytics

Layered Frame Analytics is a method of capturing important values (such as KPIs or performance indicators) from complex data, without the need to use code or complex loops.

Although this system isn't going to be able to do all types of analytic workloads (we suggest using Python for more advanced needs), it covers a lot of common analytic use-cases.

This type of analytic begins with a frame.

A frame represents a period or an instant in time. As well as a start and end time, can also contain any number of individual attributes which are the values of interest about that time period.

For example, a single frame might represent a day, and it might hold values for the highest and lowest temperature.

Example of a 24 Hour Frame

As well as attributes, frames can also store tables of data.

In the example below, the frame contains a table of information about the temperature throughout the day.

Example of a 24 Hour Frame

TABLE

Layers are used to create, modify and manipulate frames.

Although each individual layer will only perform a single task, they can be added together to create complex sequences.

For example, the get_query layer runs an ARDI query, getting a table of values from across the time of the frame.

The summarisevalues layer is used to summarise the values in a table and record them in the attributes of the frame.

So we can do the following…

Initial Frame

Layer: Use get_query To Read A Table Values for Temperature

New Frame with Table Values

TABLE

Layer: Get summarisevalues To Capture Min & Max

Final Result

The final result is a database record for the day containing the start and end times of the day, as well as the minimum and maximum temperature.

Working with Multiple Frames