What is Data Modelling? Conceptual, Logical, & Physical Data Models
Presentation to DAMA, Minnesota, / Stages of Data Modeling. 1. DAMA - Minnesota Often considered the ER or Relational Model. CvLvP PMSS Project. PROJECTS. Project. Actions. COMORDACT. Commissioners. Orders Action In Designing/Developing a Data Model: (can't do it all at once). • Start with. The logical design is about mapping of entities, relationships, and the last part Database — Modeling: Entity Relationship Diagram (ERD) (Part 5) for this relationship, is every employee can work on one or more project. In Part 1 of this blog series, I condensed a year history of Data I think a Data Model is one of three essential technical elements of any software project. Typically, logical models describe entities and attributes and the relationships . The Logical Data Model encapsulates the structural entities and.
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. Entity—relationships and semantic modeling[ edit ] Semantic model[ edit ] A semantic model is a model of concepts, it is sometimes called a "platform independent model". It is an intensional model. At the latest since Carnapit is well known that: The first part comprises the embedding of a concept in the world of concepts as a whole, i.
The second part establishes the referential meaning of the concept, i. Extension model[ edit ] An extensional model is one that maps to the elements of a particular methodology or technology, and is thus a "platform specific model". The UML specification explicitly states that associations in class models are extensional and this is in fact self-evident by considering the extensive array of additional "adornments" provided by the specification over and above those provided by any of the prior candidate "semantic modelling languages".
It incorporates some of the important semantic information about the real world. Plato himself associates knowledge with the apprehension of unchanging Forms The forms, according to Socrates, are roughly speaking archetypes or abstract representations of the many types of things, and properties and their relationships to one another.
Limitations[ edit ] ER assume information content that can readily be represented in a relational database. They describe only a relational structure for this information. They are inadequate for systems in which the information cannot readily be represented in relational form[ citation needed ], such as with semi-structured data. For many systems, possible changes to information contained are nontrivial and important enough to warrant explicit specification.
An alternative is to model change separately, using a process modeling technique. Additional techniques can be used for other aspects of systems. For instance, ER models roughly correspond to just 1 of the 14 different modeling techniques offered by UML. Even where it is suitable in principle, ER modeling is rarely used as a separate activity.
One reason for this is today's abundance of tools to support diagramming and other design support directly on relational database management systems. How do you make sense of them now? Should any be combined? What are the relationships? Typically, a vague diagram emerges in the early stages of data modeling. Only the entity names are shown, with some connecting arcs illustrating a relationship. Conceptual data modeling enumerates possible data entities in an application or system.
Progressive refinement continues once the basic entities have been specified. Often, that list of entities will keep changing during logical data modeling. The primary activity of logical data modeling is specifying relationships among data entities. The secondary activity is elaborating the attributes in each data entity. Practically, though, adding the attributes is often what enables the final definition of entities. According to normalization and other modeling principles, a data model may be terrible — yet all the stored data may be completely correct.
However, as data is added, modified, and deleted, the data model makes a big difference in keeping the data correct. He and various successors defined progressive levels of data normalization.
Practically speaking, normalization often seems to decompose the data entities. It constantly breaks attributes out of one entity and into a different entity. How many does Mr. Normalization would turn Mr. This seems to make it harder to manage data and program applications — until you hit the eighth entity that reuses a phone or address.
Relational normalization restructures data into consistent tabular form with minimal redundancy. It is distinct from the other transformations known as normalization. Codd and his successors. This is in the assignment of keys, which for Dr.
Conceptual, Logical and Physical Data Model
Codd always meant unique keys capable of individually identifying each row. The notion of keys exists in each step of normalization, but in our case we will move the specific definition of keys from the logical to the relational modeling phase.
The modeler must choose whether to use keys to inform entity definition or to dictate entity definition. Constraining Attributes to Domains We may think of attributes loosely, as numbers or text or dates.
However, in logical modeling we need to assess and assign what those attributes really are. This has two aspects. The first aspect is the mechanics: How many digits in a phone number or post code? What are the upper and lower limits of a number? Should a number allow fractions?
Does a date include the time of day? The second aspect is in the meaning, usage, or measurement of an attribute. Those usages are very different. So we need to know: Does a number refer to a temperature or a distance? Does a name refer to a place, a person, etc.? All such attribute qualities segregate them from other attributes and limit the values they can meaningfully store. The values are members of the domain.
Attributes are in fact constrained by the limits and restrictions of the domain. These may again occur as mechanical or semantic constraints on values. The occurrence of the same domain in multiple entities often provides a valuable expression of their relationship or of the congruence of different data entities.
Logical modeling gains precision by ascribing each attribute to a tightly-defined domain. Understanding Relationships Between Entities At this point, we have a bunch of entities, many of their attributes, and some idea of relationships. The main thing characterizing a relationship is relative cardinality. A vehicle may have two, four, eight, or more wheels. The relationship of vehicle to wheel, therefore, is 1-to-many.
So the relationship is zero or one vehicles to two or more wheels. Going back to our business card example, how about that address? Doe the only employee at that address? Most people share a business address with others.
What does the model say? Looking at the model, it now seems that Mr. Doe can have two or more addresses. Doe listing his Bermuda office alongside his local one. We were talking about Ms. Tamarack and her 40 employees sharing the same address, not one person sharing different addresses! So we need to introduce a new table to bridge them: In the Chen E-R notation, it might look like this: Note that while I am showing a relationship between two entities, it is possible to have three or four entities in a single relationship.
Relationships Have Names Typically, there is only one relationship between two entities.
If we have two entities called Person and Phone, we may have two different relationships between Person and Phone — one for their office phone, one for their mobile number.
Some modeling notations explicitly label each relationship. In relationships, names and entity roles are as important as relative cardinality. Or is it an entity? In my experience, bridge relationships between entities often turn into entities themselves. How might this happen? A typical circumstance is that the relationship changes over time. For a person and an address, that might turn into Occupancy.
Data Model Design & Best Practices – Part 2
This leads to questions like Is this a permanent residence or a vacation residence? Do we need to contact Dr.
Norbert about the expiration of his lease? How long has he lived here? That affects how the relationship is used, how the Person and Address information is used, and generally increases the expressiveness of the model.
The relationship of Person to Address becomes a meaningful business object on its own — its own entity. The modeler should expect this. If we are renting out apartments or offices, we might very well be interested in an Occupancy entity.
What are we going to do with our entities and relationships?
We have a business or organization, and it has business objects, business processes, and business objectives. Whatever these happens to be, they will guide our decisions now.
Data Model Design & Best Practices (Part 2) - Talend
Different applications and different business will mean different models for seemingly identical data. This is not a complete business model by any means, but it depicts what I consider logical data modeling: Now that we have restricted it to the definitions of entities, domains, and relationships, we have a good handle on the logical model.
At this point, we can think about how to query the data entities; we can start to perform business operations and answer business questions. Relational and Physical Modeling The steps involved in relational and physical modeling — which are sometimes conflated — are what turns a logical model into a working database. Simply put, a relational model may be applied to any DBMS and any deployed configuration. A physical model is the blueprint for making that application.
Setting Naming Conventions In the relational stage, having naming conventions for entities and attributes becomes a necessity. Names must make sense.