I have got this question:
What is the equivalent of table in ERD, UML and in relation data model?
In Chen's ER model (and Chen-notation ERDs), data is represented as attributes of and relationships between entities. This is an interpretation of relations in the relational model, which understands data as associations between domains of values/entities. Relations (i.e. attributes and relationships) can be represented as tables, though tables and relations don't map 1-to-1 - certain rules and semantics must be applied to tables (such as eliminating merged cells, ensuring that every cell contains exactly one value, column values are from a single domain, no duplicate rows, and order of rows/columns aren't significant) to understand them as relations.
In non-Chen ERDs (the kind in products like Visual Paradigm and MySQL Workbench), tables are directly represented but called entities, and foreign keys constraints are called relationships. This is reminiscent of the pre-relational network model. UML class diagrams fall in this category when used for data modeling.
Related
I was going through definition of Graph based database and found that it has entities having relationship with each other. Now, I am a bit confused that why Graph based database falls into category of Non-relational database whereas it has relation among entities.
Thanks in advance.
I think your confusion is probably because you think "relational" means "relationships." It seems most developers these days believe this.
In SQL, we commonly call entities "tables" but in the original computer science that described relational databases, they were called "relations" after the term from mathematics. In short, a table has a heading, which has a finite number of named columns, and it has a set of rows, where each row has the same columns as the heading. This is a relation, and its analogy is to a table, not a relationship between tables.
The relational model includes an algebra of operations you can do on relations, and each operation yields a new relation. These include selection, projection, rename, join, and set operations like union/intersect/difference.
It also defines a set of criteria for modeling data in a set of relations, such that you avoid update anomalies, i.e. you won't have data disagree with other data in the same database. These are rules of normalization.
Graph databases don't necessarily represent relations, and they don't necessarily support relational operations in the same way.
The ER diagram for a database is given below:
Now, what's the relationship between planes and flights entities?
I'd say one to many, but that'd be wrong because while one plane can have more than one flights, many flights can't have one (single) plane simultaneously.
So, what exactly is the relationship?
I'm new to databases. Please tell me if I'm wrong.
First, your diagram isn't an ER diagram, it's a table diagram. ER diagrams must be able to represent the ER model, which supports ternary and higher relationships, weak entity sets and identifying relationships, and other concepts for which table diagrams don't have notation. Proper ER diagram notation is generally referred to as Chen's notation.
Now, ER relationships are easily identified by looking for entity domains (columns that identify entity sets) that appear in the same table. These are usually indicated by PK and/or FK, but they need not be.
When you have a binary relationship (two entity domains in the same table), look at the unique constraints on these domains:
One-to-one relationships require two separate unique constraints, one for each domain.
One-to-many relationships require a unique constraint on the domain on the many side.
Many-to-many relationships require a composite unique constraint on both domains together.
One-to-many relationships can be denormalized into the entity relation of the entity on the many side, since the unique constraint required for the relationship matches the PK for the entity relation. One-to-one relationships can be denormalized into either entity relation. A many-to-many relationship requires a composite key, and must be represented as a separate relation since the composite key doesn't match either entity relation's PK.
In your example, (flight_num, planeID) represents the relationship, and since only flight_num is uniquely constrained (due to being the PK of the flights relation), this is a many-to-one relationship: each flight is associated with exactly one plane, while each plane can be involved in many flights.
Here's a visual reference in which unique constraints are indicated with underlining:
Many people still use terminology and concepts from the old network data model, such as conflating relationships with FK constraints and entity sets with tables (which is why table diagrams are often mistakenly called ERDs). I highly recommend reading Codd's paper "A Relational Model of Data for Large Shared Data Banks" and Chen's paper "The Entity-Relationship Model - Toward a Unified View of Data".
As you said the relation is One-To-Many because if it is a Many-To-Many relation it must have a junction table between these 2 tables (Tbl_Plane_Flight) , another thing is that Plane_Id is referenced in Flights table.
But there must be a validation rule (or a constraint in flights table) for this relation, that a plane cannot have many flight as the same time
I have the following as my ER Diagram. I am still learning DBMS, and trying to translate this ER Diagram to a Relational Schema. I know that each entity in the ER Diagram will have a separate table. However, I am not sure what to do about the relationship for this particular ER Diagram. We were told that each relationship between the entities will have a table too. Therefore, do I need to make separate for relationships in this ER Diagram as well? But, there are no attributes of the relationship. Also, I am having a confusion in what sort of relationship is this exactly? Is this one to many?
I am attaching the link to the picture of the ER Diagram. Please guide me in the right direction. Thanks!
Your diagram isn't an ER diagram in the original sense of that term. In the entity-relationship model, relationships are associations among entity sets and were meant to be implemented as tables. For example, your AUTHOR_BOOK, CAST and PURCHASE tables are relationship tables that associate two entity sets each (keep in mind that relationships aren't limited to just two entity sets). Note how the relationships are represented using the keys of the entity sets e.g. (actorID, inventID). The same pattern can be found in some of your other tables, i.e. (inventID, publisher), (inventID, director), (inventoryID, genre), (inventoryID, supplier), (receiptID, inventID) and (receiptID, customerID). These are your relationships - not the crow's foot lines which are just foreign key constraints. In Chen's original notation, the relationships would be indicated using diamond shapes between and connected to the two entity types. Also, Chen would've made a separate relationship table (aka junction table) for each of these relationships.
Your table diagram shows 14 tables. Following Chen's method, there would be 19 tables:
Your title references a relational schema. Note that relational schemas are not restricted to the entity-relationship model, but can represent any set of normalized tables (1NF or higher). The number of tables would partially depend on the level of normalization.
But, there are no attributes of the relationship.
This is not correct. Your Purchase relationship shows two attributes - quantity and amountPaid. Note that an attribute is a mapping from an entity or relationship set to a value set. Thus, I'm not counting the entity keys as attributes of the relationship. I also modeled Book's pubYear as an attribute of the relationship between Book and Publisher.
In practice, I would probably denormalize relations with the same determinant, which give a physical schema similar to your original diagram, though implementing every relationship table separately does have some advantage in easing schema changes when relationship cardinalities change.
I was only able to find the following two differences:
The relationships in an E-R model are explicitly defined, while they are implicit in a relational model.
Relational models require an intermediate table (often called a "junction table") to hold two foreign keys that implement the many-to-many relationship.
And why do we use the relational model, when we have an E-R diagram ?
You have it backwards.
The relationships in an E-R model are explicitly defined, while they
are implicit in a relational model.
No. Each Relational Model (RM) database base table and query result represents an application relationship. Entity-Relationship Modeling (E-RM) schemas are just a way of organizing (but under-using and under-specifying) (but with misunderstanding) relational tables and constraints.
Relational models require an intermediate table (often called a "junction table") to hold two foreign keys that implement the
many-to-many relationship.
No. It is Object-Relational Mapping (ORM) approaches that obscure their underlying straightforward relational application relationships, tables and constraints. The notion of "junction table" arose from ORM misunderstandings of confused presentations of the E-RM which itself misunderstands the RM.
As C J Date put it An Introduction to Database Systems, 8th ed:
a charitable reading of [Chen's original paper] would suggest that the E/R model is indeed a data model, but one that is essentially just a thin layer on top of the basic relational model [p 426]
It is a sad comment on the state of the IT field that simple solutions
are popular even when they are too simple. [p 427]
The Relational Model
Every relational table represents an application relationship.
-- employee EID has name NAME and ...
E(EID,NAME,...)
The mathematical term for such a thing, and also for a mathematical ordered-tuple set representing one, is a "relation". Hence the "Relational Model" (and "Entity-Relationship Modeling"). In mathematics relations are frequently described by parameterized statement templates for which one mathematical term is "characteristic predicate". The parameters of the predicate are columns of the table. In the RM a DBA gives a predicate for each base table and users put the rows that make a true statement from column values and the predicate into the table and leave the rows that make a false statement out.
/* now also employee 717 has name 'Smith' and ...
AND employee 202 has name 'Doodle' and ...
*/
INSERT INTO E VALUES (EID,NAME,...)
(717,'Smith',...),(202,'Doodle',...)
A query expression also has a predicate built from the relation operators and logic operators (in conditions) in it. Its value also holds the rows that make its predicate true and leaves out the ones that make it false.
/* rows where
FOR SOME E.*, M.*,
EID = E.EID AND ... AND MID = M.MID
AND employee E.EID has name E.NAME and ...
AND manager M.MID has
AND E.DEPT = M.DEPT AND E.NAME = 'Smith'
/*
SELECT E.*, M.MID
FROM E JOIN M ON E.DEPT = M.DEPT
WHERE E.NAME = 'Smith'
Present rows of tables making true statements and absent rows making false statements is how we record about the application situation in the database and how we interpret what the database is saying about the application situation. One can't use or interpret the database without having and understanding the predicates ie application relationships.
Entity-Relationship Modeling
E-RM (which does not really understand the RM) is essentially a(n unnecessary, restricted and restrictive) diagramming notation for describing (some parts of) (limited forms of) relational databases. Originally there were "entity (class)" icons/relations where the candidate key (CK) values were 1:1 with application entities plus other columns ("properties" of the "entity") and there were "relationship (class)" icons/tables which had foreign keys (FKs) to entity tables representing application relationships on multiple entities plus other things ("properties" of the "association"). An application relationship was represented by an icon with lines to the various entity icons that participated in it. (Ie the lines represented FKs. Which are not relationships but statements about constraints on tables.)
E-RM doesn't understand the relational model. It makes a pointless and misleading distinction between application entities and relationships. After all, every superkey (unique column set) of every base table or query result is in 1:1 correspondence with some application entity, not just the ones that have entity tables. Eg people can be associated by being married; but each such association is 1:1 with an entity called a marriage. This leads to inadequate normalization and constraints, hence redundancy and loss of integrity. Or when those steps are adequately done it leads to the E-R diagram not actually describing the application, which is actually described by the relational database predicates, tables and constraints. Then the E-R diagram is both vague, redundant and wrong.
Shorthand E-RM and ORMs
A lot of presentations and products claiming to be E-RM warp the E-RM, let alone the RM. They use the word "relationship" to mean a FK constraint. This arises as follows. When an E-RM relationship is binary it is a symbol with two lines to its FKs. So those three things can be replaced by one line between FKs. This kind of line represents that particular binary relationship and its FKs but now the E-R relationship is not explicit in the diagram although the E-R relationship is explicit in the longhand version and it is reflected by a table in what the diagrams are pictures of, namely the relational database they are describing. This gets called a "junction table". And people talk about that line/table being/representing "an X:Y relationship" between entities and/or associations without actually ever noticing that it's a particular application relationship. And there can be many such application relationships between the same two entities and/or associations.
ORMs do this too but also replace n-ary associations by just their FKs so that the associated application relationship and table are further obscured. Active Records goes even further by defining several shorthand relationships and their tables at once, equivalent to a chain of FK lines and association icons in the longhand E-RM diagram. This is exacerbated by many modeling techniques, including versions of E-RM and ORMs, also thinking that application relationships can only be binary. Again, this arose historically from lack of understanding of the RM.
They are two different things per se. A relational model represents information as tuples, directly mapped to a relational schema. The guidelines stem from relational algebra.
Meanwhile, an ER diagram models the relationships between the users and their underlying data in a system using entities. An ER diagram can be mapped to a relational model, and finally to a working schema.
I understand what cardinality is, so please don't explain that ;-)
I would like to know, what the purpose of doing cardinality is in data modeling, and why i should care.
Example: In an ER model you make relations and ad the cardinality to the relations.
When am i going to use the cardinality further in the development process? Why should i care about the cardinality?
How, when and where do i use the cardinalities after i finish an ER model for example.
Thanks :-)
Cardinalities tell you something important about table design. A 1:m relationship requires a foreign key column in the child table pointing back to the parent primary key column. A many-to-many relationship means a JOIN table with foreign keys pointing back to the two participants.
How, when and where do i use the cardinalities after i finish an ER model for example.
When physically creating the database, the direction, NULL-ability and number of FKs depends on the cardinalities on both endpoints of the relationship in the ER diagram. It may even "add" or "remove" some tables and keys.
For example:
A "1:N" relationship is represented as a NOT NULL FK from the "N" table to "1" table. You cannot do it in the opposite direction and retain the same meaning.
A "0..1:N" relationship is represented as a NULL-able FK from "N" to "0..1" table.
A "1:1" relationship is represented by two NOT NULL FKs (that are also keys) forming a circular reference1 or by merging two entities into a single physical table.
A "0..1:1" relationship is represented by two FKs, one of which is NULL-able (also under keys).
A "0..1:0..1" relationship is represented by two FKs, both NULL-able and under keys, or by a junction table with specially crafted keys.
An "M:N" relationship requires an additional (so called "junction" or "link") table. A key of that table is a combination of migrated keys from child tables.
Not all cardinalities can be (easily) represented declaratively in the physical database, but fortunately those that can tend to be most useful...
1 Which presents a chicken-and-egg problem when inserting new data, which is typically resolved by deferring constraint checking to the end of the transaction.
Cardinality is a vital piece of information of a relation between two entites. You need them for later models when the actual table architecture is being modelled. Without knowing the relationship cardinality, one cannot model the tables and key restriction between them.
For example, a car must have exactly 4 wheels and those wheels must be attached to exactly one car. Without cardinality, you could have a car with 3, 1, 0, 12, etc... wheels, which moreover could be shared among other cars. Of course, depending on the context, this can make sense, but it usually doesn't.
A data model is a set of constraints; without constraints, anything would be possible. Cardinality is a (special kind of) constraint. In most cultures, a marriage is a relation between exactly two persons. (In some cultures these persons must have different gender.)
The problem with data modelling is that you have to specify the constraints you wish to impose on the data. Some constraints (unique, foreign key) are more important, and less dependent on the problem domain as others ("salary < 100000"). In most cases Cardinality will be somewhere in between crucial and bogus.
If you are creating the data layer of an application and you decided to use an ORM, maybe it's entity framework.
There's a point when you need to create your models and your model maps. At that point you would be able to pull out your ERD, review the cardinality you put on your diagram and create the correct relationships so your data layer shape matched your database shape.