Integrity in a database is concerned with the database having correct and consistent data. Integrity constraints ensure that changes made to the database by users do not result in data inconsistency. They guard against accidental damage to the database. Specifically, data integrity in a relational database is concerned with three aspects of the data in a database:
Validation checks
A set of condition checks performed on data elements to check if they fit into the given criteria.
Range check
Checks if data items fit within a specified range limit. If the item is to be accepted it should be greater than the minimum cut off point but small than the maximum cut off point.
Example: Range of values for the field age should only take values between 1 and 65.
Specified range = 1-65
Length check
Checks if data items fit within a specified character length.
Example: The field telephone number should accept only values which are 7 digits.
A valid telephone number would thus be a number like "2502856"
Type check
Checks if data items are of a specific data type such as character, numerical, integer, real, etc.
Example: A telephone number field could have a type check which ensures that only whole numbers are entered. If an integer check is used a number like "2502856" will be accepted where as one like "CB34433" will be rejected.
Format check
Checks if data items are of a specified format. In such a situation there is a specific order of values and characters which would be accepted by the system.
Example: For a format check of dd/mm/yyyy on a date field:
the date 1210812004 will be accepted where as the date 1204198 will be rejected.
Value check
Checks for the presence of at least some value. Such fields can not be left blank and don't provide for storing null values.
Example: A field with values 114343I will be accepted
Where as one with values [] will not be accepted.
Verification
Is a process of checking the correctness of data by comparing it with it's initial source or similar forms of replicates to check if it is identical to one another. lf they are not some form of mistake is likely to have occurred and either the data element or the source document will need to be corrected.
Verification process
If Data Produce Expected Output
Then : data is verified.
Else : data contains errors.
- Accuracy;
- Correctness;
- Validity.
- Availability
Validation checks
A set of condition checks performed on data elements to check if they fit into the given criteria.
- Correct data elements which meet the criteria should result in passing the checks where as
- Incorrect data elements which do not meet the given criteria should be rejected (fail the checks) from the system.
- Range check - values within a given range.
- Length check - values of particular length.
- Type check - data type.
- Format check - data format and order.
- Value checks - checks for presence of values.
Range check
Checks if data items fit within a specified range limit. If the item is to be accepted it should be greater than the minimum cut off point but small than the maximum cut off point.
Example: Range of values for the field age should only take values between 1 and 65.
Specified range = 1-65
Length check
Checks if data items fit within a specified character length.
Example: The field telephone number should accept only values which are 7 digits.
A valid telephone number would thus be a number like "2502856"
Type check
Checks if data items are of a specific data type such as character, numerical, integer, real, etc.
Example: A telephone number field could have a type check which ensures that only whole numbers are entered. If an integer check is used a number like "2502856" will be accepted where as one like "CB34433" will be rejected.
Format check
Checks if data items are of a specified format. In such a situation there is a specific order of values and characters which would be accepted by the system.
Example: For a format check of dd/mm/yyyy on a date field:
the date 1210812004 will be accepted where as the date 1204198 will be rejected.
Value check
Checks for the presence of at least some value. Such fields can not be left blank and don't provide for storing null values.
Example: A field with values 114343I will be accepted
Where as one with values [] will not be accepted.
Verification
Is a process of checking the correctness of data by comparing it with it's initial source or similar forms of replicates to check if it is identical to one another. lf they are not some form of mistake is likely to have occurred and either the data element or the source document will need to be corrected.
Verification process
If Data Produce Expected Output
Then : data is verified.
Else : data contains errors.
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