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Worth noting that here the use of the word "model" refers not to some indefinite domain-specific data schema, but "The Relational Model" as a paradigm. It disp

Worth noting that here the use of the word "model" refers not to some indefinite domain-specific data schema, but "The Relational Model" as a paradigm. It displaced the earlier paradigm of hierarchical data model, which derived more or less from physical media (eg punch cards). 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).

antt 8y ago
>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). An outer join is an outer product, and you have the null value. I've never seen anyone use real tensors outside of physical simulations, but then again relational databases don't use real relationships either.
- talaketu 8y ago
  Null is intended for missing information, not uncertain information. It's common to use vectors to represent a signal. Relational databases really are based on the set theoretic concepts of relations and functional dependency.
goto11 8y ago
> The relational model is not well suited to uncertain data, as a row in a table is generally interpreted as a true proposition True, but the proposition does not necessarily indicate a certain fact about the world. It might be e.g. measurement or observation or opinion or guess or whatever. The proposition is just that the measurement, guess or opinion is this.
- talaketu 8y ago
  The power of the relational model is that we can use operators to combine relations to form new relations, which are then understood to also encode true propositions. A relational join is a logical implication. This is how SQL is able to answer complex queries with certainty from elementary certain data. Bayesian networks are able to do similar things from uncertain data.
asavinov 8y ago
> 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.

antt 8y ago

talaketu 8y ago
Null is intended for missing information, not uncertain information. It's common to use vectors to represent a signal. Relational databases really are based on the set theoretic concepts of relations and functional dependency.

goto11 8y ago

> The relational model is not well suited to uncertain data, as a row in a table is generally interpreted as a true proposition True, but the proposition does not necessarily indicate a certain fact about the world. It might be e.g. measurement or observation or opinion or guess or whatever. The proposition is just that the measurement, guess or opinion is this.

talaketu 8y ago
The power of the relational model is that we can use operators to combine relations to form new relations, which are then understood to also encode true propositions. A relational join is a logical implication. This is how SQL is able to answer complex queries with certainty from elementary certain data. Bayesian networks are able to do similar things from uncertain data.

asavinov 8y ago

> 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.