Review on de Bruijn shapes in one, two and three dimensions

Working with ever growing datasets may be a time consuming and resource exhausting task. In order to try and process the corresponding items within those datasets in an optimal way, de Bruijn sequences may be an interesting option due to their special characteristics, allowing to visit all possible combinations of data exactly once. Such sequences are unidimensional, although the same principle may be extended to involve more dimensions, such as de Bruijn tori for bidimensional patterns, or de Bruijn hypertori for tridimensional patterns, even though those might be further expanded up to infinite dimensions. In this context, the main features of all those de Bruijn shapes are going to be exposed, along with some particular instances, which may be useful in pattern location in one, two and three dimensions.