Logical Data Model Concepts
Non-Identifying Relationships have dotted lines, whereas Identifying SQL ⋅ Tagged: Difference between Identifying and Non-Identifying. This article shortly describes the difference between a non-identifying and an identifying relationship. It also tells you how to define the relationship type in. In Astah Professional, ER Diagrams (Entity-Relationship Diagrams) can be Select Identifying Relationship, Non-Identifying Relationship or SQL Export.
You can format the notes using standard HTML tags. Rolename tab You can change the rolename of foreign keys to differentiate between child and parent attributes in the event that overlapping foreign keys exist. To change a rolename, double-click any of the rows to invoke the Foreign Key dialog. You can also change logical and default column rolenames through the Columns tab of the Table Editor or by right-clicking the relationship and selecting Edit RoleName. Attachment Bindings tab Bind an external piece of information, or attachment to the relationship.
You can also remove an attachment from an object, override an attachment binding's default value, or change the position of a bound attachment. To override the value of the attachment you have moved to the Selected Attachments grid, double-click the Value field of the target attachment. Attachments are created in the Attachments folder of the Data Dictionary and must be applied to the relationship before they will display on this tab.
Notes You can view the incoming and outgoing relationships of a table by expanding the relevant table node and then expanding I ncoming Relationships or Outgoing Relationships. Expand the relationship node to see the name of the related table. You can change the relationship type, specify cardinality, create verb phrases to describe the relationship, create a logical business and constraint name, and define triggers by double-clicking the relationship, and then making your changes in the Relationship Editor.
You can delete a relationship by right-clicking it in the Data Model Window and then selecting Delete Relationship. You can edit a relationship by right-clicking the relationship in the Data Model Window and then selecting Edit Relationship for a logical model or Edit Foreign Key for a physical model.
When changing from identifying to non-identifying, foreign keys change from primary keys to non-key columns in the child object.
When changing from identifying to non-specific, or from non-identifying to non-specific, the foreign keys are removed from the child object. When changing from non-identifying to identifying, the foreign keys change from non-key to primary key columns or attributes in the child object. When changing from non-specific to identifying, the child object inherits the parent object's primary keys as primary key columns or attributes. When changing from non-specific to non-identifying, the child object inherits the parent table's primary keys as non-key columns or attributes.
Cardinality expresses the count of child entity type instances that may exist for a parent entity type. For example a Brand may apply to zero, one or many Item entity type instances. Conversely, an Item may be referred to by zero or one Brand.
These cardinalities are illustrated in the diagram below. 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.
ER Diagram (Entity-relatonship Diagram) | Astah User's Guide | corrosion-corrintel.info
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. Figure 97 - Entity Subtype Relationship Example Domains A domain is a named type of data representation that may apply to one or more attributes.
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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.
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.
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.