JIGSAW is a family of algorithms for recovering the unknown spatial mapping of very large sets of sensors. This enables it to localise a set of sensors within a space such as a farm or a battleground, to integrate, or 'mosaic' a set of video cameras, and to perform large-scale analysis on databases.

JIGSAW is capable of handling hundreds of thousands of simultaneous inputs, whether they be sensor signals, pixel values, or columns of data. It operates in real-time.

JIGSAW can also be applied to the integration of control systems to sensory systems (see C-SAW).

Problems Jigsaw Solves

JIGSAW is a platform technology. It can be applied to solving many different problems where there is a need to:

Extract spatial information from a set of sensor signals
Infer the relative positions of a set of sensors by considering only the behaviour of the delivered signals.

These basic functions allow JIGSAW to solve a variety of problems relating to spatial mapping.

Spatial mapping allows us to see the large-scale patterns across multiple data points. Spatial mapping underlies navigation, identification of the extension of, and boundaries between, objects and regions. As children, we develop competence to operate in space quite unconsciously, and without external instruction. Consequently, spatial awareness is often taken for granted, and just assumed to be present in intelligent systems. Only once spatial awareness is absent or dysfunctional, are we faced with the problem of how to create and maintain it.

The problem addressed by JIGSAW is how to derive spatial mappings with no prior concepts of distance, direction, locality, and their derivatives such as up, down, further, between, around, etc.