Lossless Join Decomposition

In database design, a lossless join decomposition is a decomposition of a relation $R$ into relations $R_1, R_2$ such that a natural join of the two smaller relations yields back the original relation. This is central in removing redundancy safely from databases while preserving the original data.

Criteria

Lossless join can also be called nonadditive.

If $R$ is split into $R_1$ and $R_2$, for this decomposition to be lossless (i.e., $R_1 \bowtie R_2 = R$) then at least one of the two following criteria should be met.

Check 1: Verify join explicitly

Projecting on $R_1$ and $R_2$, and joining them back, results in the relation you started with.

Check 2: Via functional dependencies

Let $R$ be a relation schema.

Let be a set of functional dependencies on $R$.

Let $R_1$ and $R_2$ form a decomposition of $R$ .

The decomposition is a lossless-join decomposition of $R$ if at least one of the following functional dependencies are in ⁺ (where ⁺ stands for the closure for every attribute or attribute sets in ):
* $R_1 \cap R_2 \rightarrow R_1$
* $R_1 \cap R_2 \rightarrow R_2$

Examples

* Let $R = (A, B, C, D)$ be the relation schema, with attributes , , and .
* Let $F = \$ be the set of functional dependencies.
* Decomposition into $R_1 = (A, B, C)$ and $R_2 = (A, D)$ is lossless under because $R_1 \cap R_2 = A)$. is a superkey in $R_1$, meaning we have a functional dependency $\$.  In other words, now we have proven that $(R_1 \cap R_2 \rightarrow R_1) \in F^+$.

References

{{Reflist

Databases
Data modeling
Database constraints
Database normalization
Relational algebra