Modelling Business Information: Entity Relation...
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The second issue is a 'chasm trap'. A chasm trap occurs when a model suggests the existence of a relationship between entity types, but the pathway does not exist between certain entity occurrences. For example, a Building has one-or-more Rooms, that hold zero-or-more Computers. One would expect to be able to query the model to see all the Computers in the Building. However, Computers not currently assigned to a Room (because they are under repair or somewhere else) are not shown on the list. Another relation between Building and Computers is needed to capture all the computers in the building. This last modelling issue is the result of a failure to capture all the relationships that exist in the real world in the model. See Entity-Relationship Modelling 2 for details.
An entity relationship diagram (ERD), also known as an entity relationship model, is a graphical representation that depicts relationships among people, objects, places, concepts or events within an information technology (IT) system. An ERD uses data modeling techniques that can help define business processes and serve as the foundation for a relational database.
Modelling Business Information provides an introduction to data modelling, to the nomenclature used by common modelling techniques, and to techniques for representing common patterns. This is a useful book for business analysts who are creating the information model as well as for business and IT users who need to understand a data model.
Modelling Business Information by Keith Gordon, is aimed at those who are new to business analysis or information modelling. Keith draws on a wealth of experience in information management, both as a practitioner, and as a lecturer with the Open University in his writing.
The second part of the book covers a range of more advanced topics from naming conventions and yet more entity-relationship model notations, to considerations of quality in information models, corporate data models, modelling for business intelligence applications, and finally goes on to look at data and database topics including an overview of SQL, and moving to database design and optimisation.
Use data models as a communication tool with business users. A 10,000-table entity-relationship model can make anyone's head spin. But a data model, or a portion of one, focused on a specific business process or data analysis offers the perfect opportunity to discuss and verify the schema with business users. The assumption that business users can't grasp a data model is a fatal mistake in modeling efforts.
The entity relationship (ER) data model has existed for over 35 years. It is well suited to data modelling for use with databases because it is fairly abstract and is easy to discuss and explain. ER models are readily translated to relations. ER models, also called an ER schema, are represented by ER diagrams.
Business Entity Models are constructed to provide standard definitions for key business entities (such as customer and employee) that are used by all activities in a business (i.e. the \"system\" under study). These models also define the \"owner\" of the business entity and activities that create or otherwise manipulate the entity.
As in UAM, which also applies to entity models, there are three level entity models: conceptual (i.e. business level), logical (i.e. logical level), and physical (i.e. technical level), see: Concept: Data Modeling. These levels of modeling reflect the different levels of modeling defined in UAM, see Concept: Architecture Perspectives and Viewpoints. Summaries of logical entity modeling (i.e. the Logical Perspective) and technical data modeling (i.e. the Technology Perspective) are provided in sections below for reference, but also see: Guideline: Logical Entity Model and Guideline: Technical Entity Model. A definition of conceptual data modeling is provided in Wikipedia: Conceptual Schema and a description of how to do it is provided in Conceptual Data Modeling.
A simple example model is shown below. At the business level we are many focused on identifying the entities, agreeing upon their precise definitions and describing required relationships between them. Optionally, attributes may be defined if they are important in understanding the entity and how it fits into the business.
Entity-relationship diagrams or ER diagrams are used to showcase the relationships developed between objects or entities in a system. Also known as the entity-relationship model, this type of flowchart is used in various fields such as research, education, business information system, or software engineering.
Conceptual data models are the foundation of every data model that's created. They help you understand which entities exist in your business and how they relate to each other. Conceptual models don't include the details regarding the specific attributes attached to an entity.
BEs are concepts (things) that can easily be understood from a business perspective. They can have attributes (properties) as well as relationships to other business entities. These entities, attributes and relationships can all have textual descriptions and be depicted via diagrams. The BEs, their properties, and relationships are described using the Unified Modelling Language (UML) in a UML modelling tool.
Data modeling (data modelling) is the process of creating a data model for the data to be stored in a database. This data model is a conceptual representation of Data objects, the associations between different data objects, and the rules.Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.
Entity-relationship Data Modelling was introduced by Peter Chen in 1976 that revolutionized the computer science industry. Entity-relationship models are a logical structure where the relationship among data points is created based on specific software development requirements. Unlike relational Data Modelling techniques, entity-relationship Data Modelling is designed to support business processes in a particular order. Even if two datasets can have numerous relationships, entity-relationship is only created based on the data points needed for accomplishing a task while minimizing data privacy risks.
Each instance of these object types are uniquely identified and defined in business terms. The definitions supply the semantic content for a data model. The ARTS Operational Data Model, in technical terms, is a relational data model built using entity relationship modeling notation.
An entity type is a representation of a person, place, thing, event or concept of interest to a retailer. Examples of entities include Customer, Item, Retail Store, Web Site, Purchase Order, Retail Transaction - and the list can go on to hundreds of nouns. Within the ARTS data model each entity type is defined in business terms. In an entity diagram, entity types are represented as rectangles. Each entity type has a unique, singular noun phrase assigned as its name. In a relational data model, each entity type instance is uniquely identified by a primary key. A primary key is one or more attributes that have values used to uniquely identify and distinguish each entity type instance from each other.
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.
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.
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. are related in a data model. 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 principles of dimensional modeling are based on fact and dimension tables. We will cover facts and dimensions in the