A dimensional logical data model is in many cases a fully-attributed perspective of the dimensional conceptual data model, as illustrated in this figure. Whereas the logical relational data model captures the business rules of a business process, the logical dimensional captures the business questions to determine the health and performance of a business process.

Admissions Count in this figure is the measure that answers the business questions related to Admissions. The entities surrounding the Admissions provide the context to view Admissions Count at different levels of granularity, such as by Semester and Year.

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A logical data model is a detailed representation of data requirements, usually in support of a specific usage context, such as application requirements. Logical data models are still independent of any technology or specific implementation constraints. A logical data model often begins as an extension of a conceptual data model.

In a relational logical data model, the conceptual data model is extended by adding attributes. Attributes are assigned to entities by applying the technique of normalization, as shown in this figure. There is a very strong relationship between each attribute and the primary key of the entity in which it resides. For instance, School Name has a strong relationship to School Code. For example, each value of a School Code brings back at most one value of a School Name.

A conceptual data model captures the high-level data requirements as a collection of related concepts. It contains only the basic and critical business entities within a given realm and function, with a description of each entity and the relationships between entities.

For example, if we were to model the relationship between students and a school, as a relational conceptual data model using the IE notation, it might look like Figure 46.

Each School may contain one or many Students, and each Student must come from one School. In addition, each Student may submit one or many Applications, and each Application must be submitted by one Student.

The relationship lines capture business rules on a relational data model. For example, Bob the student can attend County High School or Queens College, but cannot attend both when applying to this particular university. In addition, an application must be submitted by a single student, not two and not zero.

Recall Figure 40, which is reproduced below as Figure 47. This dimensional conceptual data model using the Axis notation, illustrates concepts related to school:

On the Anchor Model, Student, Course, and Attendance are anchors, the gray diamonds represent ties, and the circles represent attributes.

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The Data Vault is a detail-oriented, time-based, and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach, encompassing the best of breed between third normal form and star schema. Data Vaults are designed specifically to meet the needs of enterprise data warehouses. There are three types of entities: hubs, links, and satellites. The Data Vault design is focused around the functional areas of business with the hub representing the primary key. The links provide transaction integration between the hubs. The satellites provide the context of the hub primary key (Linstedt, 2012).

In this figure, Student and Course are hubs, which represent the main concepts within a subject. Attendance is a link, which relates two hubs to each other. Student Contact, Student Characteristics, and Course Description are satellites that provide the descriptive information on the hub concepts and can support varying types of history.  Anchor Modeling is a technique suited for information that changes over time in both structure and content. It provides graphical notation used for conceptual modeling similar to traditional data modeling, with extensions for working with temporal data. Anchor Modeling has four basic modeling concepts: anchors, attributes, ties, and knots. Anchors model entities and events, attributes model properties of anchors, ties model the relationships between anchors, and knots are used to model shared properties, such as states. 

Object‐Role Modeling (ORM) is a model‐driven engineering approach that starts with typical examples of required information or queries presented in any external formulation familiar to users, and then verbalizes these examples at the conceptual level, in terms of simple facts expressed in a controlled natural language. This language is a restricted version of natural language that is unambiguous, so the semantics are readily grasped by humans; it is also formal, so it can be used to automatically map the structures to lower levels for implementation (Halpin, 2015).

FCO-IM is similar in notation and approach to ORM. The numbers in that figure are references to verbalizations of facts. For example, 2 might refer to several verbalizations including “Student 1234 has first name Bill.”

The Unified Modeling Language (UML) is a graphical language for modeling software. The UML has a variety of notations of which one (the class model) concerns databases. The UML class model specifies classes (entity types) and their relationship types (Blaha, 2013).

Figure 41 illustrates the characteristics of a UML Class Model:

• A Class diagram resembles an ER diagram except that the Operations or Methods section is not present in ER.
• In ER, the closest equivalent to Operations would be Stored Procedures.
• Attribute types (e.g., Date, Minutes) are expressed in the implementable application code language and not in the physical database implementable terminology.
• Default values can be optionally shown in the notation.
• Access to data is through the class’ exposed interface. Encapsulation or data hiding is based on a ‘localization effect’. A class and the instances that it maintains are exposed through Operations.

The class has Operations or Methods (also called its “behavior”). Class behavior is only loosely connected to business logic because it still needs to be sequenced and timed. In ER terms, the table has stored procedures/triggers.

Class Operations can be:

• Public: Externally visible
• Internally Visible: Visible to children Objects
• Private: Hidden

In comparison, ER Physical models only offer Public access; all data is equally exposed to processes, queries, or manipulations.

The concept of dimensional modeling started from a joint research project conducted by General Mills and Dartmouth College in the 1960's. In dimensional models, data is structured to optimize the query and analysis of large amounts of data. In contrast, operational systems that support transaction processing are optimized for fast processing of individual transactions.

Dimensional data models capture business questions focused on a particular business process. The process being measured on this dimensional model is Admissions. Admissions can be viewed by the Zone the student is from, School Name, Semester, and whether the student is receiving financial aid. Navigation can be made from Zone up to Region and Country, from Semester up to Year, and from School Name up to School Level.

The diagramming notation used to build this model - the 'axis notation' - can be a very effective communication tool with those who prefer not to read traditional data modeling syntax.

Both the relational and dimensional conceptual data models can be based on the same business processes (as in this example with Admissions). The difference is in the meaning of the relationships, where on the relational model the relationship lines capture business rules, and on the dimensional model, they capture the navigation paths needed to answer business questions.