Logical data model
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This article may require cleanup to meet Wikipedia's quality standards. Please improve this article if you can. (May 2008) |
In computer science, a logical data model also referred to as LDM, is a representation of an organization's data, organized in terms of a particular data management technology. When ANSI first laid out the idea of a logical schema (in 1975 [1]), the choices were hierarchical and network. Relational had just been recognized as a data organization theory but no technology existed. Currently, the choices are relational, object-oriented, and XML. Relational data is described in terms of tables and columns. Object-oriented data is described in terms of classes, attributes, and associations. XML is described in terms of tags.
Logical data models, properly designed, should be based on the structures identified in the conceptual data model, since this describes the semantics of the business, which the logical model should also reflect. Even so, since the logical data model anticipates implementation on a finite-capacity computer, some will modify the structure to achieve certain efficiencies.
In certain organizations, there is a tendency to use the term 'Logical Data Model' to mean the 'Domain Model' or as an alternative to the domain model. While the two concepts are closely related, and having overlapping goals, domain model is more focused on capturing the concepts in problem domain than structure of the data which is associated with that domain.
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[edit] What is a Logical Data Model
- Graphical representation of the business requirements
- Contains the things of importance in an organisation and how they relate to one another
- Contains business textual definitions and examples
- Validated and approved by a business representative
- Basis of physical database design
This is sometimes incorrectly called a "physical data model", which is not what the ANSI people had in mind. The physical design of a database involves deep use of particular database management technology. For example, a table/column design could be implemented on a collection of computers, located in different parts of the world. That is the domain of the physical model.
[edit] Difference of Logical & Physical Data Model
People often get confused with the fact that logical and physical data model is very different in their objective, goal and content. Following are some key differences.
| Logical Data Model | Physical Data Model |
|---|---|
| Includes entities, attributes and relationships | Includes tables, columns, keys, data types, validation rules, database triggers, stored procedures, domains, and access constraints |
| Uses business names for attributes | Uses abbreviated column names limited by the database management system (DBMS) |
| Is independent of technology (platform, DBMS) | Includes primary keys and indices for fast data access. |
| Is normalized to 4th normal form | May be de-normalized to meet performance requirements |
| Does not include any redundant or derived data | May include redundant columns or results of complex or difficult to recreate calculation columns |
| Business Subject Matter Experts (SMEs) validate and approve the model | Physical Modeler lead the modeling activity |
[edit] Why Build Logical Data Model
- Helps common understanding of business requirements
- Provides foundation for designing a database
- Facilitates data re-use and sharing
- Decreases development and maintenance time and cost
- Confirms a logical process model and helps impact analysis.
[edit] Logical Modeling Benefits
- Clarifies functional specifications and avoids assumption
- Confirms business requirements
- Facilitates business process improvement
- Focus on requirements independent of technology
- Decreases system development time and cost
- Becomes a template for the enterprise
- Facilitates data re-use and sharing
- Faster ROI
- Gathers metadata
- Foster seamless communication between applications
- Focuses communication for data analysis and project team members
- Establishes a consistent naming scheme
[edit] References
- American National Standards Institute. 1975. “ANSI/X3/SPARC Study Group on Data Base Management Systems; Interim Report”. FDT(Bulletin of ACM SIGMOD) 7:2.
