Logical Data Model (LDM)

A logical data model (LDM) is a step in the process of data modeling. Logical data models are created after business requirements have been gathered and analyzed. They can be used to create physical databases or they may simply serve as documentation for an existing system.

Logical data models define entities and relationships between those entities, but they do not specify how the database will be implemented. This allows different database implementations to be used without affecting the logical model. For example, a relational database could be used, or a NoSQL database could be used.

Logical data models are important because they help to ensure that data is accurate and consistent across different systems. They also make it easier to understand how data flows through a system.

There are many different ways to create a logical data model. The most common method is to use Entity-Relationship Diagrams (ERDs). ERDs show the relationships between entities in a graphical way. Other methods include Data Flow Di

What is a logical data model (LDM)?

A Logical Data Model (LDM) is a data model that defines data elements in detail and is used to create visual understandings of data entities, attributes, keys, and relationships. An LDM acts as a blueprint that represents the definitions and characteristics of data elements that remain consistent across technological changes. The benefits of an LDM include providing better clarity and structure to databases, improving efficiency when making changes or updates to the database, reducing redundancy in the database design process, improving communication between stakeholders about the database design process, and making it easier for developers to create applications based on established data models.

What are the components of an LDM diagram?

1. Entities An entity is an object in a data model which represents a single, specific thing. It can have one or more components, such as attributes and relationships. Subtyping is the use of inheritance to create relationships between different types of entities. Aggregation and composition are less common and typically must be implied from the data model. Relationships between entities are described using terms such as place, live at, and are part of; these connections have cardinality (the number of objects involved in the relationship) and optionality (whether or not they can exist without each other).

2. Attributes

The different types of attributes used in Logical Data Model (LDM) diagrams can include fields, properties, values, and relationships. Fields represent the data columns in a table. Properties are those that describe the data within the model. Values are those that are assigned to an attribute to denote its content. Finally, relationships depict how entities interact with each other within a diagram structure. Each attribute is important for providing clarity and accuracy within logical models, as getting the correct level of detail is key for development and maintenance purposes down the line. DAMA08 emphasizes on using attributes that are independent of software or hardware performance considerations in order to maintain inherent properties of data across all models it pertains to.

3. Relationships

The relationships in an LDM diagram are significant as they represent the connections between the data in the model. These relationships show how different pieces of data are related to each other and can help visualize how these connections will support a business's goals. By understanding the cardinality, optionality, and multiplicity of a relationship, as well as distinguishing between various types of relationships (such as UML dependencies), it is easier to comprehend how different elements interact with each other.

4. Keys

The purpose of keys in an LDM diagram is to identify the relationships between concepts and to help navigate through the model. Keys can be either natural or surrogate, providing a trade-off between convenience and maintainability, and they can be external or internal. Keys are used to referencing other models and provide additional information about how they are utilized within a model.

5. Normalization

The significance of normalization in a Logical Data Model (LDM) diagram is to support efficient querying. Normalizing the data schema can improve the speed at which queries are performed, allowing for more efficient access to data. Denormalizing a database may also provide performance benefits, but this should be carefully weighed against the potential costs of inconsistent data due to denormalization.

6. Denormalization

Denormalization is the process of reducing the number of distinct data entity types by lowering the level of normalization in a data model. In an LDM diagram, this can result in a change to the number of levels in the diagram. Denormalization can be beneficial as it can improve the performance and usability of a database; however, if the initial LDM diagram design meets performance needs then denormalization should not be resorted to unless further profiling reveals that there is a need for improved database access time.

7. Data naming conventions

In an LDM diagram, data naming conventions are important in order to ensure that the data is accurately and consistently represented. Not only will this make the data easier to read, but it will also simplify the process of understanding and using the schema. Furthermore, following common modeling standards can help developers adhere to a consistent set of conventions when creating their models. Additionally, normalizing the data model helps optimize performance while making it clearer and easier to understand. It is important to note that even though normalization may come at a cost in terms of performance, it provides clear benefits in terms of clarity and usability for both humans and machines alike.

8. Data model patterns

Data model patterns are often used in Logical Data Model (LDM) diagrams to solve common business issues. Data modelers should have an understanding of data modeling to collaborate with Agile data engineers and use a variety of tasks when creating and maintaining data models. The aim of data modeling is to reduce redundant data and increase performance. Many books provide guidance on how to become a better data modeler, including information on the different types of patterns used in LDM diagrams.

9. Data modeling notations

An LDM diagram provides a visual representation of the logical structure of a database. It is used to keep track of entities, relationships, data elements, and attributes within an organization's data system. This type of diagram makes it easier to understand how all the components in a system interact with each other and how they depend upon one another for their functionality. By understanding the relationships between these components, an organization can make more informed decisions about which changes should be made to its architecture to improve performance or enhance security. Another benefit of using LDM diagrams is that they make it easier for teams to collaborate on projects since everyone can see the entire structure at once instead of having to search for pieces individually.

What are the different types of logical data models?

1. Entity Relationship Diagrams (ERDs)

An Entity Relationship Diagram (ERD) is a type of data model used to visually represent the structure and behavior of databases. ERDs are often used to identify relationships between entities in order to better understand the information stored within them. Through an ERD, one can gain insight into how many customers are placing orders, which customer lives at which address, or even which entities depend on each other. Furthermore, with subtyping and UML dependencies as options for use in data models, LDMs or Logical Data Models can be utilized to explore domain concepts. Ultimately by utilizing an ERD's visual representation of relationships between data sets developers and database admins alike have a streamlined method for designing queries and tables accordingly when constructing databases.

2. Object-Oriented Data Models

An object-oriented data model is a type of data modeling that is based on the concept of objects which are derived from classes. It differs from other models in that it organizes and stores data according to the relationships between objects rather than as individual entities. In contrast, hierarchical and network models organize and store data as individual entities within a single structure or set of structures, respectively. On the other hand, relational models organize and store data in tables with columns that map to values associated with each row of the table.

3. Object-Relational Data Models

An object-relational data model (ORM) is an approach to organizing data within a database system that combines the concepts of traditional relational databases with those of object-oriented programming languages. ORMs enable users to store and interact with their data in an intuitive way, without having to create separate models for each type of data. This provides flexibility, as the same model can be used for multiple types of objects and relationships. Additionally, ORMs are gaining popularity due to their ability to better align with object-oriented programming languages such as Java and C#, making them easier for developers who are familiar with these languages. Generalizing specialists prefer ORMs since they offer more versatility when it comes to working on diverse projects across different domains.

4. Relational Data Models

The relational data model is a theory of data organization that was initially established in the 1970s. It is based on the assumption of some data modeling formalism, such as Relational Modeling or Object-Oriented Modeling. The ANSI/SPARC three-level architecture defines three levels of data models: external view, conceptual model, and physical model. This approach to modeling allows for logical relationships between elements that can be represented with diagrams and utilized to create logical databases. Benefits of this approach include improved efficiency when querying databases and increased scalability for future modifications to databases.

5. Network Data Models

A network data model is a type of data model which helps businesses better understand their operations by recognizing and analyzing areas that can be improved. As a visual aid, it provides more detail into data and its entities, helping to illustrate the system's structure in an organized manner. Furthermore, it is designed to allow users to observe the bigger picture and make more informed decisions. Network data models can be used for business, technical or reporting purposes.

6. Hierarchical Data Models

A hierarchical data model is a way to organize data into multiple layers, usually based on how it will be used. This type of model can be useful for reporting purposes or database management. The ANSI/SPARC three-level architecture is a common way to compare different kinds of data models, as it provides insight into the organization and structure of the different levels. Additionally, this type of model can help businesses identify areas where improvements need to be made and improve accuracy during analytics processes.

7. Document Data Models

A document data model is a logical data model used to store and manage data in a structured way. It is similar to analysis patterns in that it details solutions to common domain issues. David Hay's book DataModel Patterns provides helpful guidance for anyone involved in analysis-level modeling, as it covers a wide range of business domains. The book can be used as an invaluable reference when designing document data models.

8. Graph Data Models

A graph data model is a logical data model of an application's presentation layer. It presents the entities and attributes an envisioned dashboard will require, with facts typically central and dimensions surrounding them. This type of data model enables easier analysis by allowing for the easy visualization of relationships between items in the data. Sample dimensional models and corresponding star schemas can be depicted schematically on whiteboards during project planning. Graph data models look very much like business target models, only with more details about attributes associated to each entity.

9. Semantic Data Models

A semantic data model is a type of logical data model that is expressed independently of any particular database management product or storage technology. It defines the meaning and relationships between different pieces of data within an organization, allowing for improved understanding and communication of this information. The benefits of using semantic data models are numerous, including improved accuracy in analysis and decision-making processes, easier access to relevant information due to better understanding between team members, cost savings from being able to reduce the time required for training new staff members on complex topics related to the organization’s operations, as well as improved accuracy in reporting results from the analysis.

10. Star Schemas

A star schema is a logical data model used to design dashboards. This type of data model uses tables and columns to describe the data and employs an object-oriented approach. A star schema is typically used within the ANSI/SPARC three-level architecture, which includes a logical schema, conceptual model, and physical model. It helps developers and administrators compare models of data by surrounding facts with dimensions that provide more details about the attributes of entities. The use of star schemas also allows for greater flexibility than traditional relational models when it comes to organizing information in a usable way.

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