Database Normalization - Page 2
March 22, 2002
Did you notice anything strange in the data above? Congratulations if you did! Madagascar is misspelt in the 3rd record. Now imagine trying to spot this error in a table with thousands of records! By using the structure above, the chances of the data being corrupted increases drastically.
The solution is simply to take out the duplication. What we are doing formally is looking for partial dependencies, ie fields that are dependent on a part of a key, and not the entire key. Since both project number and employee number make up the key, we look for fields that are dependent only on project number, or on employee number.
We identify two fields. Project name is dependent on project number only (employee_number is irrelevant in determining project name), and the same applies to employee name, hourly rate and rate category, which are dependent on employee number. So, we take out these fields, as follows:
Clearly we can't simply take out the data and leave it out of our database. We take it out, and put it into a new table, consisting of the field that has the partial dependency, and the field it is dependent on. So, we identified employee name, hourly rate and rate category as being dependent on employee number. The new table will consist of employee number as a key, and employee name, rate category and hourly rate, as follows:
And the same for the project data.
Note the reduction of duplication. The text "Madagascar travel site" is stored once only, not for each occurrence of an employee working on that project. The link is made through the key, the project number. Obviously there is no way to remove the duplication of this number without losing the relation altogether, but it is far more efficient storing a short number repeatedly, than a large piece of text.