Conceptual, Logical and Physical Data Model
The entity relationship (ER) data model has existed for over 35 years. For the rest of this chapter, we will use a sample database called the . AVERAGE and SUM are used; Can create logical problems when relational tables are linked. Relational. DBMS. • Entity-Relationship model is used in the conceptual design of Examples are a person, car, customer, product, gene, book etc. • Attributes. A logical ER model is developed to enrich a conceptual model Logical data model example.
There are a variety of ways cardinality is used to express the relative counts of parent entity types to children entity types and they are presented in the Data Model Methodology and Notation Topic. It also illustrates how foreign keys and cardinality are presented in an entity diagram. It introduces Owned Attributes which are attributes that are NOT inherited from another entity type and are not part of an entity type's primary key. Figure 96 - Sample Entity, Attribute and Simple Relationship In addition to cardinality, there is a special type of relationship called a subtype that allows several child entity types to inherit a common parent entity type characteristics.
This is illustrated in the next diagram. A retail transaction definition is shown in the yellow block. As modeled here, a RetailTransaction may have zero, one or many RetailTransactionLineItem entity instances associated with it. The RetailTransactionLineItem entities are dependent entities because a line item cannot exist without a retail transaction header. A retail transaction line item provides a set of attributes including line item number that all subtypes share i.
A RetailTransactionLineItem entity type instance may be one and only one subtype. This relational concept of subtype is analogous to the inheritance used to model classes and objects in object oriented design. For this example subtype child entity types efficiently represent a retail transaction and the different kinds of line items needed to capture item, discount, tax and tender data.
The sample receipt shows how each subtype of RetailTransactionLineItem reflects different sales receipt line items.ER Diagram Sample Problem Statements Video 1
Figure 97 - Entity Subtype Relationship Example Domains A domain is a named type of data representation that may apply to one or more attributes. Data representation defines a data type such as integer, string, floating point, date, time or other standard data type or an extended definition that assigns custom properties and constraints to a base data type.
Domains enable retail-specific data types to be derived from SQL base data types. The creation of domains can also be used to define constraints that values assigned to an attribute assigned to a domain.
Data Model Semantics Semantics is the branch of linguistics and logic concerned with meaning. Logical models, in addition to identifying entities, attributes, relationships and domains define what each instance of these object means. These definitions provide the semantic content of a data model are are essential to the business relevancy of a relational model.
The diagram below illustrates the assignment of a definition to the ItemID attribute of Item. Definitions should be expressed in business terms and reflect the business concepts represented by a data model entity, attribute, relationship, domain and other model objects. Data models are not just for information technologists. Data models are a prerequisite to operating a retail enterprise in today's business climate.
Information as A Currency and Asset Retailing in the 21st century is as much about managing information as it is about managing cash, merchandise, customers, stores, vendors and other "real world" business assets. Most retail decision makers rely on information to make decisions because they can not personally visit and observe every retail site personally.
To be useful, information has to be identified, named, described and organized into a coherent structure so it can be understood by decision makers. Data modeling provides a formal set of tools and procedures to make information useful. The formality and discipline introduced by data modeling is vital in figuring out what retail reports actually are telling decision makers.
Conceptual, Logical and Physical Data Model
Consider the terms item, article, product, SKU and merchandise. They each mean different things to different people. The data model by defining each entity type clarifies what each term means.
Where some are used as synonyms, they are explicitly referenced as such. This is called a controlled vocabulary and it is a key value-adding feature of data modeling. It establishes a common language for retailer organizations and individuals to communicate using explicitly defined words. Costs of Misinterpretation and Inconsistent Semantics Retailers manage a complex set of interactions between customers, vendors, tax authorities, regulators, employees and a wide range of other kinds of parties.
Retailers that do not have a single standard way to identify, name, define and describe items, tender types, tax rules, promotions, vendor deals and the like will encounter transaction processing errors that will cost real money.
Logical ERD ( Entity Relationship Diagram)
Data accuracy has a direct, unambiguous impact on the bottom line. If an ordered item is not correctly aligned with the vendors catalog product code and the order is placed some party the customer, retailer, vendor, etc.
The data model particularly a third normal form relational model reduces this risk by insisting on a consistent representation of each data element in a single place in the data model.
This same issue comes up when developing reports. Retailers without a consistent way of identifying, naming and defining entities, attributes and relationships spend a lot of time and money trying to reconcile conflicting summary reports.
In some companies middle and senior managers spend an inordinate amount of time manually reconciling inconsistently defined data. Data models that establish an enterprise-wide controlled vocabulary eliminate one of the root causes of data inconsistency. Data Model as a Reflection of Business Assumptions, Constraints and Rules Data models reflect important retail business assumptions and constraints.
For example, the relationship between taxation, merchandise and retailer provided services is explicitly represented in the way items, taxes, tax authorities, retail transactions, inventory control documents, etc. The rules governing the way point of sale discounts are treated by a retailer are likewise reflected in the way price modification rules are related to retail transaction sale return items and promotions.
The complex web of relationships that define retailer business rules is explicitly presented through entity relationship models. The Sarbanes Oxley Act of mandates detailed reporting and tracking of business operational and financial controls. Data for compliance is generated by corporate systems for financials, sales, marketing, inventory, purchasing, and related operational systems. Depending on the size and complexity of the organization, these systems are part of a set of applications whose data must be turned into information, then distilled into knowledge on reports for senior executives.
The operational and decision support systems are based on data being collected and stored in data structures such as tables and files, and then transformed and moved — to become knowledge in reports that are signed by corporate principals. The business and technical meta data for these systems data constitute an information architecture that can both guide the development of internal controls and give corporate principals the confidence that the reports they sign are valid.
In this section we will go through the ERD symbols in detail.
Studentobject e. Invoiceconcept e. Profile or event e. In ERD, the term "entity" is often used instead of "table", but they are the same.
When determining entities, think of them as nouns. In ER models, an entity is shown as a rounded rectangle, with its name on top and its attributes listed in the body of the entity shape.
Entity Attributes Also known as column, an attribute is a property or characteristic of the entity that holds it. An attribute has a name that describes the property and a type that describes the kind of attribute it is, such as varchar for a string, and int for integer.
Logical ERD | Editable Entity Relationship Diagram Template on Creately
The ER diagram example below shows an entity with some attributes in it. Primary Key Also known as PK, a primary key is a special kind of entity attribute that uniquely defines a record in a database table. In other words, there must not be two or more records that share the same value for the primary key attribute. The ERD example below shows an entity 'Product' with a primary key attribute 'ID', and a preview of table records in database.
Foreign Key Also known as FK, a foreign key is a reference to a primary key in table. It is used to identify the relationships between entities. Note that foreign keys need not to be unique. Multiple records can share the same values. The ER Diagram example below shows an entity with some columns, among which a foreign key is used in referencing another entity. Relationship A relationship between two entities signifies that the two entities are associated with each other somehow.
For example, student might enroll into a course.
- Conceptual Model
- When to draw ER Diagrams?
- Logical Model
The entity Student is therefore related with Course, and the relationships is presented as a connector connecting between them. Cardinality Cardinality defines the possible number of occurrence in one entity which are associated to the number of occurrences in another. When present in an ERD, the entities Team and Player are inter-connected with a one-to-many relationship. In an ER diagram, cardinality is represented as a crow's foot at the connector's ends.
The three common cardinal relationships are one-to-one, one-to-many, and many-to-many. One-to-One cardinality example A one-to-one relationship is mostly used to split an entity in two to provide information concisely and make it more understandable. The figure below shows an example of one-to-one relationship. One-to-Many cardinality example A one-to-many relationship refers to the relationship between two entities X and Y in which an instance of X may be linked to many instances of Y, but an instance of Y is linked to only one instance of X.
The figure below shows an example of one-to-many relationship. Many-to-Many cardinality example A many-to-many relationship refers to the relationship between two entities X and Y in which X may be linked to many instances of Y and vice versa.