> The relational model is not well suited to uncertain data, as a row in a table is generally interpreted as a true proposition. For statistical data sets, analytical processing may be better served by array/tensor models (which also exhibit uniformity).
Arrays/tensors and (relational) tables can be thought of as alternative ways to represent a set with this major difference:
o [Relational table] Each column is an axis. A row is point.
o [Arrays] Each dimension is an axis. A cell is a point.
This is why it is wrong to interpret a table with a two-dimensional array - their data have completely different semantics.
The success of a (data) model depends on its ability to represent and manipulate relationships in a simple and natural way.
There exist of course other uniform ways to represent data, for example, using functions (and operations with functions). But many of them have been developed under the umbrella of the relational model even though they have little to do with the relational principles.
Arrays/tensors and (relational) tables can be thought of as alternative ways to represent a set with this major difference:
o [Relational table] Each column is an axis. A row is point.
o [Arrays] Each dimension is an axis. A cell is a point.
This is why it is wrong to interpret a table with a two-dimensional array - their data have completely different semantics.
The success of a (data) model depends on its ability to represent and manipulate relationships in a simple and natural way.
There exist of course other uniform ways to represent data, for example, using functions (and operations with functions). But many of them have been developed under the umbrella of the relational model even though they have little to do with the relational principles.