RDBMS & Graphs: Relational vs. Graph Data Modeling
You will want to have a denormalized schema to support reporting, particularly in data marts. For example, if all of your entity types are at second normal form ( 2NF) or To maintain the relationship back to Order1NF, the OrderItem1NF table . Data normalization is the process of identifying relationships in the data and The repeating group becomes a separate subordinate data entity. to identify the relationship where the data values of one or more data attributes Many pre- relational data denormalization rules existed, relational data. Each node (entity or attribute) in a native graph property model directly and of the ease and beauty of a well-done, normalized entity-relationship diagram: a simple, . The Problem of Relational Data Model Denormalization.
An important thing to notice is the application of primary and foreign keys in the new solution.
Relationships and Data Normalization – DATAVERSITY
When a new table is introduced into a schema, in this case OrderItem1NF, as the result of first normalization efforts it is common to use the primary key of the original table Order0NF as part of the primary key of the new table. Because OrderID is not unique for order items, you can have several order items on an order, the column ItemNumber which is unique to a type of item was used to form a composite primary key for the OrderItem1NF table.
A different approach to keys was taken with the ContactInformation1NF table.
The column ContactID, a surrogate key that has no business meaning, was made the primary key. Figure 3 presents the data schema of Figure 2 in second normal form 2NF. The problem with OrderItem1NF is that item information, such as the name and price of an item, do not depend upon an order for that item. This information depends on the concept of an item, not the concept of an order for an item, and therefore should not be stored in the order items table — therefore the Item2NF table was introduced.
OrderItem2NF retained the TotalPriceExtended column, a calculated value that is the number of items ordered multiplied by the price of the item. The value of the SubtotalBeforeTax column within the Order2NF table is the total of the values of the total price extended for each of its order items. A better way to word this rule might be that the attributes of an entity type must depend on all portions of the primary key. To resolve this problem the PaymentType3NF table was introduced in Figure 4containing a description of the payment type as well as a unique identifier for each payment type.
Denormalization From a purist point of view you want to normalize your data structures as much as possible, but from a practical point of view you will find that you need to 'back out" of some of your normalizations for performance reasons.
This means that a SID value is associated with multiple values of MajorMinor and Activity attributes, and together they determine other attributes. The entity instance of Student Details entity type is shown Figure 7. Each normal form rule and its application is outlined. First Normal Form 1NF The first normal form rule is that there should be no nesting or repeating groups in a table.Normalization - Database Management System
Now an entity type that contains only one value for an attribute in an entity instance ensures the application of first normal form for the entity type. So in a way any entity type with an entity identifier is by default in first normal form. For example, the entity type Student in Figure 2 is in first normal form. Second Normal Form 2NF The second normal form rule is that the key attributes determine all non-key attributes. A violation of second normal form occurs when there is a composite key, and part of the key determines some non-key attributes.
The second normal form deals with the situation when the entity identifier contains two or more attributes, and the non-key attribute depends on part of the entity identifier.
For example, consider the modified entity type Student as shown in Figure 8. The entity type has a composite entity identifier of SID and City attributes. Figure 8 An entity instance of this entity type is shown in Figure 9.
Now, if there is a functional dependency City? Status, then the entity type structure will violate the second normal form. Figure 9 To resolve the violation of the second normal form a separate entity type City with one-to-many relationship is created as shown in Figure The relationship cardinalities can be further modified to reflect organizational working.
In general, the second normal form violation can be avoided by ensuring that there is only one attribute as an entity identifier. This normal form is violated when there exists a dependency among non-key attributes in the form of a transitive dependency.
Relational vs. Graph Data Modeling
For example consider the entity type Student as shown in Figure 4. In this entity type, there is a functional dependency BuildingName? Fee that violates the third normal form. Transitive dependency is resolved by moving the dependency attributes to a new entity type with one-to-many relationship.
In the new entity type the determinant of the dependency becomes the entity identifier. The resolution of the third normal form is shown in Figure The Boyce-Codd normal form rule is that every determinant is a candidate key. Even though Boyce-Codd normal form and third normal form generally produce the same result, Boyce-Codd normal form is a stronger definition than third normal form.
Every table in Boyce-Codd normal form is by definition in third normal form. Boyce-Codd normal form considers two special cases not covered by third normal form: Part of a composite entity identifier determines part of its attribute, and a non entity identifier attribute determines part of an entity identifier attribute.
Normalized vs. denormalized DB design in FileMaker
These situations are only possible if there is a composite entity identifier, and dependencies exist from a non-entity identifier attribute to part of the entity identifier.
For example, consider the entity type StudentConcentration as shown in Figure The entity type is in third normal form, but since there is a dependency FacultyName?
MajorMinor, it is not in Boyce-Codd normal form. Figure 12 To ensure that StudentConcentration entity type stays in Boyce-Codd normal form, another entity type Faculty with one-to-many relationship is constructed as shown in Figure Figure 13 Fourth Normal Form 4NF Fourth normal form rule is that there should not be more than one multi-valued dependency in a table.
For example, consider the Student Details entity type shown in Figure 6. Now, during requirements analysis if it is found that the MajorMinor values of a student are independent of the Activity performed by the student, then the entity type structure will violate the fourth normal form.
To resolve the violation of the fourth normal form separate weak entity types with identifying relationships are created as shown in Figure The StudentFocus and StudentActivity entity types are weak entity types.
It is now presumed that the Student entity type has the functional dependency SID? Due to the similarity in the notion of an entity type and a relation, normalization concepts when explained or applied to an ERD may generate a richer model.
More specifically, either key can be used as parent or child depending on the relationship. In addition, the relationships usually involves a range of values based on proximity criteria rather than exact value matches. Data optimization is the process of identifying data entities that have the same relationship between the business world and the organization, but have different names, and combining them into a single data entity.
Data normalization typically separates data and creates data entities. Data optimization ensures that identical data entities are combined. A common example is Employee, Worker, Staff, and Personnel established by different projects that are combined into an Employee data entity. Many people include data optimization as part of formal data normalization, many treat it as a separate task, and many completely ignore it leading to increased data disparity.
Data deoptimization is the process of identifying deployment relationships between the data and data sites where those data are stored. Data may be stored in one data site, in multiple data sites, or split across multiple data sites based on optimum utilization. When redundant volatile operational data are created, a synchronization mechanism must be implemented to keep those data in synch. Many people include data deoptimization as part of formal data deoptimization, many treat it as a separate task, and many completely ignore it leading to increased data disparity.
Data denormalization is the process of identifying relationships between the logical data as understood by the business and the physical data that will be implemented for optimum processing. The logical data schemas are adjusted to physical data schemas, according to formal data denormalization rules, without compromising the logical data schemas.
Data denormalization has changed over the years as technology progressed from flat files, to index sequential, direct index sequential, hierarchical, network, network with full inversion, relational, and so on.
Data denormalization will continue to change as new technology evolves. Many pre-relational data denormalization rules existed, relational data denormalization rules exist, and many post-relational data denormalization rules will likely exist.
Each organization should have only one set of logical data schemas within a single organization wide data architecture. Those logical data schemas can be denormalized multiple times for different processing platforms or purposes. However, multiple denormalizations of volatile operational data create redundant data that require a synchronization mechanism to keep those data in synch. Data renormalization is the process of performing data normalization on data that has already been normalized for one purpose based on relationships identified for a different purpose, such as renormalizing operational data to analytical data analytical data normalizationor renormalizing analytical data to predictive data predictive data normalization.
Data renormalization adjusts the data relations between data entities and the data attributes within data entities. Data renormalization is not a data denormalization process, because denormalized data cannot be further denormalized.
- Relationships and Data Normalization
- RDBMS & Graphs: Relational vs. Graph Data Modeling
- Normalizing with Entity Relationship Diagramming
In other words, physical data schemas cannot be further denormalized to other physical data schemas.