science

Logical schema

Logical schema is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Logical schema rather than just read about it. In short: A logical data model or logical schema is a data model of a specific problem domain expressed independently of a particular database management product or storage technology (physical data model) but in terms of data structures such as relational tables and columns, object-oriented classes, or XML tags. This is as opposed to a conceptual data model, which describes the semantics of an organization without reference…

Key takeaways

Logical schema belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Logical schema to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Logical schema from memory before moving on to harder problems.

Reference excerpt

A logical data model or logical schema is a data model of a specific problem domain expressed independently of a particular database management product or storage technology (physical data model) but in terms of data structures such as relational tables and columns, object-oriented classes, or XML tags. This is as opposed to a conceptual data model, which describes the semantics of an organization without reference to technology.

Overview Logical data models represent the abstract structure of a domain of information. They are often diagrammatic in nature and are most typically used in business processes that seek to capture things of importance to an organization and how they relate to one another. Once validated and approved, the logical data model can become the basis of a physical data model and form the design of a database. Logical data models should be based on the structures identified in a preceding conceptual data model, since this describes the semantics of the information context, which the logical model should also reflect. Even so, since the logical data model anticipates implementation on a specific computing system, the content of the logical data model is adjusted to achieve certain efficiencies. The term 'Logical Data Model' is sometimes used as a synonym of 'domain model' or as an alternative to the domain model. While the two concepts are closely related, and have overlapping goals, a domain model is more focused on capturing the concepts in the problem domain rather than the structure of the data associated with that domain.

History

When ANSI first laid out the idea of a logical schema in 1975, the choices were hierarchical and network. The relational model – where data is described in terms of tables and columns – had just been recognized as a data organization theory but no software existed to support that approach. Since that time, an object-oriented approach to data modelling – where data is described in terms of classes, attributes, and associations – has also been introduced.

Logical data model topics

Reasons for building a logical data structure Helps common understanding of business data elements and requirement Provides foundation for designing a database Facilitates avoidance of data redundancy and thus prevent data and business transaction inconsistency Facilitates data re-use and sharing Decreases development and maintenance time and cost Confirms a logical process model and helps impact analysis.

Conceptual, logical and physical data model A logical data model 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. Conceptual, logical and physical data models are very different in their objectives, goals and content. Key differences noted below.

See also DODAF Core architecture data model Database design Entity-relationship model Database schema Object-role modeling FCO-IM

References

External links Building a Logical Data Model By George Tillmann, DBMS, June 1995.

Worked examples

Example 1 — a first encounter with Logical schema

Start with the simplest possible case. Write down what Logical schema claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Logical schema before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Logical schema ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Logical schema

In research: Logical schema appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Logical schema in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Logical schema is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data modeling, so understanding it makes those chapters shorter.
In everyday life: Look for Logical schema outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Logical schema in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Logical schema means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Logical schema out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Logical schema in simple terms?

Why does Logical schema matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Logical schema?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Logical schema.