Need a brief difference between network model, relational model,entity set model like why and when can each one be used?
I have looked into various resources but there needs to be a brief and concise idea about the said topic
In graph terms, the relational model is an undirected n-ary graph in which the nodes are values and the edges are rows. Logically, tables represent predicates and rows represent propositions about entities which are represented as values of a domain. Edges are joined to form paths and processed in sets.
The entity-relationship model is also an undirected n-ary graph in which some tables represent sets of nodes and other tables represent sets of edges. It's a "semantic framework" built on top of the relational model, and while it offers a seemingly simpler and richer structure than the pure relational model, it's actually more complicated and less expressive. It's usually queried and processed using relational mechanisms.
The network data model is a directed binary graph in which the nodes are rows and the edges are pointers. Unlike relational models it's usually processed imperatively and edges are navigated to get to related nodes. It makes a hard distinction between attributes and relationships (unlike ER in which attributes are binary relationships).
I'm not very familiar with the entity set model and haven't seen it in the field. Did you perhaps mean the entity-relationship model? For more on the entity set model, see Data structures and accessing in data-base systems.
Different data models exist since people have different concepts of how best to organize, manipulate and access data. The relational model is the only one that has been proven equivalent to first-order logic. While knowledge of these models are invaluable in understanding data, we don't get to choose models freely, but must choose among software systems that tend to implement a mish-mash of models and features.
A quick google search shows some pretty decent explanations.
https://en.wikipedia.org/wiki/Database_model
http://www.unixspace.com/context/databases.html
If this is not what you're asking, please clarify the question.
Related
I was told by a student that a conceptual model includes major entities and their relationships but not such high detail level of information about the attributes and their values.
Is this true?
Is this the limitation to the conceptual model?
The answer depends on whose methodology you prefer. If you are creating a conceptual data model, you will most often model as you say. If you are creating a conceptual business ontology, there is some controversy among experts. I am among the camp that models only concepts in a conceptual business ontology. That is a model of things in the world, not data about things in the world. The former is used to understand a domain; the latter is used to design a particular system. Non-identifiable concepts may be flattened into attributes for the design of a particular system. For example, weight may be modeled more conceptually as "Mass", or with much more commitment to some unit of measure, such as "Pounds on Earth" or "Grams on Mars" in an attribute having a computer serialization of floating point, integer, or string.
I'm new to data modelling and have started following tutorials to learn more.
I am trying to create a model for a hypothetical scenario and am struggling to validate what I have created to see if it is what would be considered a correct data model.
Essentially all im trying to do is correctly store data in a normalised form. In my scenario there are 3 types of people and each share some attributes and have one set of contact details each.
Does the below data model look feasible?
The relationship between person and one of defendant, magistrate, or staff-member is a case of the class/subclass pattern. There are two common ways of modeling this pattern in relational tables.
One way is called "Class Table Inheritance". You can find out more by visiting this tag: class-table-inheritance or by searching the web for Martin Fowler's treatment of the same subject. Your design resembles this design.
Another way is called "Single Table Inheritance", which you can also research the same way. single-table-inheritance. It's simpler, and works ok in some cases. You deal with fewer joins, but you deal with more NULLS.
Many people who go for class table inheritance also apply a technique called "Shared Primary Key". shared-primary-key. Using this technique, Defendant, Magistrate, and Staff_Member would each use a copy of person_id as the primary key. This primary key also functions as a foreign key. Shared primary key enforces the one-to-one nature of the IS-A relationships that exist in this case.
If you want to go further in data modeling, you might want to learn ER modeling as a distinct data model from the relational model. What you've done here is essentially to use ER diagramming to diagram a relational model. There's nothing wrong with that, but it obscures a whole new field of study, generally called conceptual data modeling.
If you generate an ER model at the conceptual level, you don't attempt to implement it in terms of tables. There is a diagramming convention in ER that goes under the name "generalization/specialization" that allows you to depict a class/subclass situation, while remaining silent on how it's going to be implemented.
Conceptual data models have an area of usefulness, in addition to relational data modeling. What makes conceptual data models useful is precisely the fact that they present the information requirements without stating how those requirements are going to be met.
Once you are proficient at creating conceptual data models, it's not hard to convert one of them to a relational model.
This may be more than you bargained for, but since you are taking on learning modeling, I thought I'd survey some of the field for you.
A company is trying to build a system that breaks down consumer goods (soft drinks, detergents, beauty products, etc.) down to the very basic components. The aim is to be able to break down all the characteristics of a product into as many enumerable quantities as possible. For instance, a soft drink will have the properties flavor, calories, color, cost, etc. Do note that the products will come from a huge variety of segments and not all properties will be applicable to all products (detergents don't have calories) and similarly sounding properties are not similar (detergents with a lime fragrance is different from a lime flavored soft drink). Also, search is expected to be fast and the database needs to understand relationships between products. Suggest only a data model for the same.
The feature you highlight, that not all properties describe all products, is a classic feature of a class/subclass situation. Or, if you prefer, type/subtype.
Dealing with just that feature of the problem, I'm going to call your attention to the EER (Extended Entity Relationship) model if you want to model your understanding of the subject matter. The EER has a way of depicting what it calls a generalization/specialization pattern. That's a good search term to find detailed descriptions of it. This will adequately depict what you've said you're after.
A word of caution, however. The majority of ER models you'll see here in SO are design models, not conceptual models. That is, they reflect the intent of designing tables made up of columns and rows, with keys and foreign keys, to contain the relevant data.
What I'm recommending is the EER model for a very different purpose. It's to depict the way the data looks to the subject matter expert, not the way the data looks to the database designer. That distinction is lost on those who have never learned the difference between analysis and design.
If your project is a major one, it's worth spending an appropriate amount of time on a detailed analysis of the subject matter before moving on to design. Understanding the problem before you try to solve it is key to successful work on big projects.
Once you have a good conceptual model that captures the analysis, the choice of a data model to reflect the design will depend on what kind of database you've decided to build. It might be relational, it might be multidimensional, it might be unstructured. It depends. The analysis, however, will be more useful if it's implementation independent.
I'm doing a work about database, and now I need to show three different images, one image with the conceptual model, other with logical model and other with physical model of a database.
But Im here with some difficults to understand which image represents each model.
I'm looking for reliable information about this, but I find different answers and I'm a bit confused.
So I came here to see if you can help me.
I have below my three images, do you think I have the correct title for each image?
Conceptual model:
In conceptual model, I think that I neeed to put my tables with atributes but without relationships.
Logical Model:
In logical model, I think I need to put my tables with atributes, but now with my relationships.
Physical Model:
In physical model, I think I need to put my tables with atributes, but now with my relationships and also with foreign keys
A Conceptual Model (CM) is an informal representation of the business represented in a manner that is understood by users. It will consist of classes of entities with attributes and the business rules regarding these. It is often presented as Entity-Relationship Diagrams.
A Logical Model (LM) formalizes the CM into data structures and integrity constraints. it should include all the data structures and integrity constraints for the data (this is all constraints, not just that subset of constraints that are easily defined in most available database management systems). It is database management system agnostic.
The LM may be presented as a Relational Data Model (RDM). In which case all the data structures and integrity constraints will be formally represented only using mathematical relations.
A Physical Model (PM) is a representation of the LM on specific hardware and database management system. It may consist of information such as storage sizing and placement; access methods such as indexing; and distribution such as clustering or partitioning.
Using these definitions I would say that all you diagrams are versions of Conceptual Models; as they do not include all the integrity constraints for the data being managed and do not include any information regarding an implementation on specific hardware or database management system.
The conceptual/logical/physical layers have changed somewhat over the years, and also vary according to different schools of thought. The way I learned it, back in the 1980s was this:
The conceptual model summarizes the semantics of the data with reference to the subject matter. It is not bound to a relational implementation. The implementation could be in some sort of prerelational database, or even in classical files of records. You have entities, relationships, attributes, and domains. You also have business rules. That's about it. Like your summary, it's primarily for communication with users and other stakeholders. The idea is to pin down the requirements during the analysis phase.
The logical model is a preliminary design. It's bound to the relational model, but not to a specific DBMS. You have relations, tuples, attributes, and constraints. Relationships are implemented as foreign keys, sometimes requiring junction relations. I tended to use the terminology of tables, rows, and columns, instead of relations, tuples, and attributes, but that's mostly nomenclature. Normalization is relevant here.
The physical model is a detailed design. It's DBMS specific, and takes into account data volume, expected traffic, and performance. Denormalization is relevant here. This leads directly to a creation script.
This is by no means the majority view, let alone a general consensus. You need to understand your audience to see if this framewok works.
Is it a homework or what? The question seems so artificial...
The 3rd one is Physical because the data types are closer to actual DBMS data types.
Between the 1st, and 2nd ones... I'm stuck. The only difference is the crow-feet relationship. If there's a progression between the three images, I'd guess this would make the 2nd one the Conceptual.
But it is difficult because, with PowerDesigner, you could still represent the relationship with crow-feet in the Logical model. But anyway, there should be evidence of the migration of the "foreign key" attribute id_cat in the News entity, which is missing here.
Nope. I was reading my example diagrams too fast, there's no migration in Logical model.
So, just by elimination, I'd make the 1st one the Logical.
Say we are representing school course data. The relevant part of the example encompasses three real-world concepts: school, campus, and semester. A school can have many campuses, and there is a finite number of semesters.
In the real world, if we wanted to specify a campus + semester combination, it would be elementary. But the data model needs to be represented using a tree structure, like
Foo University:
Main campus
Fall 2010
Spring 2011
Bar College:
North campus
Spring 2011
South campus
Spring 2011
This pattern could continue. For instance, departments could exist in the real world as children of the school, but in the model they would be represented as child nodes of the semester, because what's important about them can change from semester to semester. Basically, we represent the permutations of a set of choices as a tree.
What is the name for this data model pattern?
In the heading you heading mention "choice permutations" which suggests a dynamic pattern (i.e. how to use such a structure for decision making). If it's this then I'd agree with #robert that it's a Decision Tree.
In the body however you say
...the data model needs to be represented using a tree structure...
If your questions is simply the name of this tree-based structural pattern the answer is Hierarchical Database Model.
It's characterised by 1..N relationships between parent and child and pre-dates the Relational model (it was - and still is - the basis for IBM's IMS database system).
You allude to one of the problems with it. Namely, that the only way to model graph-based structures using it means denormalising and repeating elements. Removing that limitation is central to the Relational model.
hth.
I would call it an Entity Tree. It's not so much a data modeling pattern as a natural representation of your Entity relationships.
Decision tree.
A decision tree is a decision support tool that uses a tree-like graph
or model of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility. It is one way to
display an algorithm. Decision trees are commonly used in operations
research, specifically in decision analysis, to help identify a
strategy most likely to reach a goal. If in practice decisions have to
be taken online with no recall under incomplete knowledge, a decision
tree should be paralleled by a probability model as a best choice
model or online selection model algorithm. Another use of decision
trees is as a descriptive means for calculating conditional
probabilities.