I am having a hard time understanding what is the difference between the Max and Min cardinalities when trying to design a database.
Remember cardinality is always a relationship to another thing.
Max Cardinality(Cardinality)
Always 1 or Many. Class A has a relationship to Package B with cardinality of one, that means at most there can be one occurance of this class in the package. The opposite could be a Package has a Max Cardnality of N, which would mean there can be N number of classes
Min Cardinality(Optionality)
Simply means "required." Its always 0 or 1. 0 would mean 0 or more, 1 ore more
There are tons of good articles out there that explain this, including some that explain how to even property "diagram". Another thing you can search for is Cardinality/Optionality (OMG Terms) which explains the same thing, Optionality is "Min" Cardinality is "Max",
From http://www.databasecentral.info/FAQ.htm
Q: I can see how maximum cardinality is used when creating relationships between data tables. However, I don't see how minimal cardinality applies to database design. What am I missing?
A: You are correct in noticing that maximum cardinality is a more important characteristic of a relationship than minimum cardinality is. All minimum cardinality tells you is the minimum allowed number of rows a table must have in order for the relationship to be meaningful. For example, a basketball TEAM must have at least five PLAYERS, or it is not a basketball team. Thus the minimum cardinality on the PLAYER side is five and the minimum cardinality on the TEAM side is one.
One can argue that a person cannot be a player unless she is on a team, and thus the minimum cardinality of TEAM is mandatory. Similarly an organization cannot be a basketball team unless it has at least five players. The minimum cardinality of PLAYERS is mandatory also. One could argue in the opposite direction too. When a player quits a team, does it cease to be a team until a replacement is recruited? It cannot engage in any games, but does it cease to be a team? This is an example of the fact that each individual situation must be evaluated on its own terms. What is truth in THIS particular instance? The next time a similar situation arises, the decision might be different, due to different circumstances.
Agree with other answers, here's a slightly different view. Think in terms of optionality and multiplicity. Take an example: Person has Address.
Optionality asks: Does every Person need to have an Address? If so the relationship is unconditional - which means minimum cardinality is 1. If not, then min cardinality is 0.
Multiplicity asks: Can any given Person have more than one Address? If not, the maximum cardinality is 1. If so the maximum cardinality is >1. In most cases it's unbounded, usually denoted N or *.
Both are important. Non-optional associations make for simpler code since there's no need to test for existence before de-referencing: e.g.
a=person.address()
instead of
if (person.address !=null) {
a=person.address()
}
Addresses are a good example of why Multiplicity is important. Too many business applications assume each person has exactly one address - and so can't cope when people have e.g. holiday homes.
It is possible to further constrain the cardinality, e.g. a car engine has between 2 and 12 cyclinders. However those constraints are often not very stable (Bugatti now offers a 16 cylinder engine). So the important questions are optionality and multiplicity.
hth.
Let's work with an example -
Students takes Class. Here both Students and Class are entities.A School may or may not have students enrolled in a particular semester. Think of a school offering courses in summer semester but no student is interested to join in. So, student's cardinality can be (0,N). But if a Class is going on means, it should have at least 1 student registered. So, its cardinality should be (1,N). So, you should check whether the entity participating in the relation is partial or total, which decides it's cardinality in the relation.
Hope it helps.
Maximum Cardinality:
1 to 1, 1 to many, many to many, many to 1
Minimum Cardinality:
Optional to Mandatory, Optional to Optional, Mandatory to Optional, Mandatory to Mandatory
To your question, 'what is the use of optionality in database design?':
It becomes very helpful in the scenarios like the following.
When you design 2 tables with 1-to-1 relation, you will be confused to decide where (in which table) to have the foreign key. It's very easy to decide it, if you have optionality 1 for one table and 0 for the other table. The foreign key should be present in the former. There are many other uses for it as well.
Hope it helps.
Maximum Cardinality:- one-one, one-many, many-many
Minimum Cardinality:- zero or one
This link describes my answer, why it is so, what's the representation,
and what it is.
Related
I have been studying database design and programming for quite some time now, but I still can't get a grasp of understanding each individual normal form (1NF, 2NF, 3NF.)
Seeing as anytime the data is in Third Normal Form, it is already automatically in Second and First Normal Form, can the whole process actually be accomplished less tediously by fully normalizing the data from the start. I can accomplish this easily by arranging the data so that the columns in each table, other than the primary key, are dependent only on the whole primary key.
How important is it to understand each individual normal form if we can simply fully normalize the data less tediously by doing what I have described?
EDIT: What I'm ultimately asking is: Is it important to go through the steps of each normal form when normalizing data, or is it appropriate to just go to Third Normal Form seeing as the result is ultimately the same?
I highly recommend understanding each normal form as this will help you determine or investigate any issues with a current database may have as sometimes you might not have the perfect scenario each time and understanding each normal form will help you to understand the current problems with an existing database design if there are any.
Going through step by step through the different normal forms will help you to figure out why we do this and this is to achieve the goals specified by E. F. Codd.
The objectives of normalization were stated as follows:
1. To free the collection of relations from undesirable insertion, update and deletion dependencies.
2. To reduce the need for restructuring the collection of relations as new types of data are introduced, and thus increase the life span of application programs.
3. To make the relational model more informative to users.
4. To make the collection of relations neutral to the query statistics, where these statistics are liable to change as time goes by.
Here is a image to help you understand the different normal forms better.
P.S. BCNF is actually 3.5NF not 4NF
It's right that, when being in the 3. NF, you're also in the 2. and in the 1. NF. However, the only condition for the 3. NF is not only that all the data is only dependent on the whole candidate key. It also has the condition that it already is in the 2. NF, meaning that every property that is not the candidate key has to fully depend on the candidate key and that it is in the 1. NF, meaning that every column has to be atomar. So yes, it is important to understand every NF if you want to have a table in the 3. NF.
I'll try to explain the Normal Forms to you:
1. NF
The 1. NF states that every column has to be atomar. This means, there shouldn't be multiple items of data in one column. For example, the adress of someone shouldn't be stored in one column, but should be splitted in the country, the state, the street and so on. Each of these pieces of data should then be stored in their own column.
2. NF
The 2. NF states that every attribute, that is no part of the candidate key, has to be identificable only by the whole candidate key. That means for example that you shouldn't store books and printing labels in one table. Because then the name of the book would only be dependent on the id of the book, while the printing label's name would only be dependent of the id of the printing label and not of the whole candidate key.
3. NF
The 3. NF nearly states the same as the 2.: No column is allowed to be dependent on a non candidate key column. That means for example that you shouldn't store the IBAN of a book and an id of the book in the same table, with only the id being the candidate key, as you'd only need the IBAN to find the name to the book.
If this doesn't explain the matter well enough, there's a lot of information online regarding the normal forms (like Wikipedia).
its not the case that if its in 3 NF its in 1 NF nad 2 nd NF .it was like if its in 2nd NF it has to be in 1st NF beforehand .and same goes for 3NF .for normalising to 3NF it has to clear 1st and 2nd NF forms.
1st normal forms states that no multivalued attribute should be present.
2NF states that there should not be partial dependency on a non prime attribute .
3NF states that no transitive depedency should be there .
thank you
The only NF (normal form) that matters is 5NF.
A relation (value or variable) is in 5NF when for every way it can be losslessly decomposed the components can be joined back in some order where the common columns of each join are a superkey of the original. (Fagin's PJ/NF paper's membership algorithm.)
This allows a table to be the join of others with overlapping meanings but without update anomalies. (Although update anomalies cease at ETNF, between 4NF & 5NF.)
Anyway if you wanted a lower NF you should normalize to 5NF then denormalize. The main reason people settle for lower NFs is ignorance. There are certain costs & benefits, but people don't know or address them--code must restrict updates to account for the problematic update anomalies. Normalization to a given NF is not done by going through lower NFs; one uses an appropriate algorithm for the NF one wants. (This is made clear by most textbooks, although some wrongly say to move through lower NFs, but putting into a lower NF can prevent good higher-NF versions of the original from turning up later.)
PS There is no single notion 1NF and all it has in common with higher NFs is that both seek "better" designs.
From what I recall of the process, it's a method that you follow to get to a state where the storage and search facilities of the database are fully optimised. Yes 3NF does encapsulate the rules below it, 1st and 2nd, but it is far easier to unpick the data if you start at the easier forms of normalization to see if your data is in an efficient format for storage in a RDBMS or SQL based database. Jumping in straight at a higher normal form makes the whole process for beginners harder and intimidating and to not analyse the data correctly. To be honest will make hard work when dealing with difficult data structures that are not just your usual invoice, invoice Lines, address stuff that you tend to deal with day in and day out. Going through the process of normalization, sometimes there is value in unpicking data structures that were not obvious from the start, which not only makes your data more efficient but also helps you reason over what you are trying to accomplish.
I currently studying database i've seen degree and cardinality uses as same term, or in some other degree is defined as no. of entities involved in a relationship and further catogories as unary, binary and trenary.
Some placed degree is defined as The degree of a relationship type concerns the number of entities within each entity type that can be linked by a given relationship type.
Cardinality is minimum and maximun number of entity occurrence associated with one occurrence of
the related entity
cardinality types as 1 to 1 , 1 to many , many to many. or min and max cardinality.
Min degree is optionality and maximum degree is cardinalty.
what is the difference between degree and cardinaltiy ?
In another context cardinality is a number of rows in table and degree is a number of columns.
So what i'm i suppose to write if question is asked "Define cardinality ?".
Can somebody explain ?
Ok here is the explanation
1.Degree. This is the number of entities involved in the relationship and it is usually 2 (binary relationship) however Unary and higher degree relationships can be exists.
2.Cardinality. This specifies the number of each entity that is involved in the relationship
there are 3 types of cardinality for binary relationships
one to one (1:1)
one to many (1:n)
many to many (n:m)
hope this will clear your mind. Please communicate for more information
Degree - number of attributes (columns) in a relation (table)
Cardinality - number of tuples (rows) present in a table
See this for more details.
To add to the first answer:
Simply
Degree of a Relation - Number of attributes in a relation
Cardinality of a Relation - Number of tuples in a relation.
Can't post the image to show you but you can check out this book to read up more and get a better picture. Also there is Connolly and Begg - Database Systems, 4th Edition
Reference: Elmasri, R., Navathe, S.B., 2011. Fundamentals of Database Systems. 6th ed. United States of America: Pearson.
Degree of a Relationship : The number of participating entities in a relationship. This can be unary, binary, ternary, quaternary, etc
Cardinality : The number of relationship instances an entity can participate in.
Ex: 1:1, 1:Many, Many:N
(Min,Max) notation : Minimum represents the participation constraints while Maximum stands for the cardinality ratio.
Degree of a relation : Number of columns(attributes) in a relation(table).
Degree of a relationship is different from degree of a relation (table). Both definitions are likely to get mixed up and cause confusion.
Relation in this context (in relational databases) is synonym to a "table"
Whereas,
Relationship is synonym to "a connection between tables (relations)".
We have to consider both of these characteristics known as "degree" and "cardinality" of
relations (tables) and
relationships
Separately.
1. in a relation (table)
i) Degree - Number of fields (columns) in relation.
ii) Cardinality - number of records (rows) in relation.
2. in a relationship
i) Degree - Number of entities (tables) involved in a relationship (Unary, Binary, Ternary, N-array)
ii) Cardinality - Number of connections that each record (row/data) of an entity might establish with the records of other entity. (One to one, One to many, Many to many)
It would be good to take note of a distinction when referring to this definition:
Degree of a Relationship differs from,
Degree of a Relation
Good definitions for both have been given above, just take note of this so that the different definitions don't end up confusing you.
I am trying to show the following in the ER diagram:
There are instructors and courses, a course is taught by only one instructor
whereas an instructor can give many courses.
My question is, is there any difference between two diagrams, in other words, does it matter which line we turn into an arrow, or what only matters is only the direction of the arrow?
Also, if we think about the mapping cardinalities; is it 1 to many or many to 1? If we think in terms of courses, then it is many to one but if we think in terms of instructors, then it is one to many. How do we decide this?
Thank you.
In ER diagrams when the relationship is denoted the arrows are not used. Some instructors use this arrow when they want to decide the cardinalities but that is just to get the cardinality (1:1, 1:M and N:M)
I have attached the ER diagram for this in Chen notation and also using Crow Notation you can use either of them.
Deciding the cardinality for a relationship is a practical scenario there is no hard and pass rule to obtain it. What you need to do is start from one side of the relationship and take one tuple (instance) and see how many tuples from the other entity participate for the relationship. Then do the vise versa. Then you know the participation number of tuples) from each entity to the relationship. Think about set theory and functions in mathematics when you decide the cardinality (ie Set of instructors, Set of Courses and set of Teaches relationship type) then this is so easy but if you are not from a mathematic background just think of practical scenario.
For Example
a) For 1 instructor he or she can teach Many (M) courses
b) For 1 Course there is only 1 instructor
so in instructor side there is always 1 in a) and b) but in Courses there is M and 1 in a) and b) there for Instructor:Course cardinality is 1:M
I don't think the other answer is fully correct.
I would say that one should use arrows, and one should use a notation that gives a meaningful name to each direction of the relationship. In this case it will be "teaches" in one direction, and "is taught by" in the other. Either use arrows next to the names or put the name near to the entity to which it refers. You could use one line (with two arrow heads) or two lines (with one arrow each).
I would also suggest that cardinality is just one kind of constraint, and the notation should reflect that. For example, the two names for the relationship could be "teaches (many)" and "is taught by (exactly one)". The point is you might have "teaches (one or two)" or "is taught by (exactly two)" and so on.
It is better to be explicit and clear about exactly what your constraints really are.
Both are having exactly opposite cardinality
🔸Simple clean line means many.
🔸Arrow means one.
If we consider both with same cardinality.
then, many to many should be represented by following the second convention as (please assume diamond for relationship set and rectangle for entity set)
INSTRUCTOR <---- TEACHES -----> COURSE
which is actually of no meaning.
If we consider both with opposite cardinality.
then, many to many should be represented by following the second convention as (please assume diamond for relationship set and rectangle for entity set)
INSTRUCTOR ----- TEACHES ------ COURSE
No explicit arrow is always considered many to many. So, it is correct (only if we consider both opposite)
Consider an 'employee' entity set and 'department' entity set, having relationship set as 'manage'.
Employee-------------Manage--------------------Department
(entity set) (Relationship set) (entity set)
One to many relationship means one entity of employee set can be associated with more than one entity of Department entity set but, an entity of Department set can be associated with at most one entity of employee entity set.
That means if there is one to many relationship between employee and department entity sets, then each employee can manage more than one department and at the same time each department is managed by at most one employer.
Can anyone tell me what is chasm trap? Perhaps fan trap too as I'm not too clear. Also, please provide easy to understand examples (via Chen notations).
My understanding thus far: I understand that Fan trap is M:1:1:M, which suggests the paths between entities is ambiguous.
I understand that. For example, if M represents Student and the other M represents School then it'll be ambiguous because we don't know which student studies at which school (that's what I understood so far).
However, I cannot grasp what is chasm trap.
Also, how can I identify the traps and then fix it?
Based on Conolly&Begg:
Fan trap occur in a situation when a model represents relationship between entity types however a path between certain entity occurrences is ambiguous.
Example:
(Staff)-1:N-has-1:1-(Division)-1:1-operates-1:N-(Branch)
in this model it may be impossible to determine the branch a staff belongs to, in the situation when staff belong to division having more than 1 branches.
Restructuring the model resolves trap
(Division)-1:1-operates-1:N-(Branch)-1:1-has-1:N-(Staff)
Chasm trap occur when a model suggests relationship between entity types however a path between certain occurrences does not exist.
Example:
(Branch)-1:1-has-1:N-(Staff)-0:1-oversees-0:N-(PropertyForRent)
Because Staff relationship to PropertyForRent is with optional participation (0:1) for staff the path for Branch to PropertyForRent may not exist. Solution to this would be direct relationship between Branch and PropertyForRent with mandatory participation.
In simple word, for both the cases (FAN & CHASM) it will produce more line(result sets) than actual. How to identify
FAN -> 1-N-N means table relation from one -> many -> many
CHASM -> N-1-N means one row table to two or more table many relation
LOOP -> join all tables and when make loop like circle (In this case we will lose some rows absolutely)
Nothing to identify but when you create Universe than we have to keep our eyes open, if you see out of these situation while developing Universe than there will be a problem always. So rectify by applying aliases, context.
Once all problems solved at Universe level than we are good to go for reporting. By practice you will have excellent knowledge.
I fan trap occurs when three tables joins in a fashion where there realtion to each other is 1 to many way. means table A B and C are in join as .. table A links to table B in one to many and table B to table C relates again one to main way A-->B-->C.
Currently I am having trouble learning normalization. While I know basic concepts behind 1NF - 3NF, I still do not understand the steps that one needs to follow before normalization.
According to my understanding, one has to first collect base entities, their attributes, relation among the entities and then start normalization. But I do not understand, whether I am supposed to normalize all attributes at once or normalize attributes of the entities that have some sort of relation with each other.
Considering an example of a store.
store(name, address, contact)
customer(sn, name, address)
item(id, name, price)
transaction(id, date, customer_sn, item_id, quantity, total_price)
According to my understanding I would either try to normalize all attributes at once or normalize attributes of only customer, item and transaction.
I know I am missing something, I just cannot figure it out.
Any help is appreciated. Thanks for your valuable time.
According to my understanding, one has to first collect base entities,
their attributes, relation among the entities and then start
normalization.
No, you don't have to do that. And in fact it's often hard, if not impossible, to even identify some base entities before you start normalizing.
At the logical level, you start by collecting all the attributes you need, and you put them in one big relation. You will find smaller examples of this in virtually every textbook about database systems.
After you collect all the required attributes, determine the dependencies among them.
For a store, you might collect these attributes.
A. store name
B. store physical address
C. item sku
D. item name
E. item price
F. transaction timestamp
G. transaction type (as "Purchase", "Return", "Store Credit", etc.)
H. transaction register (which machine performed the transaction)
I. transaction cashier (which employee performed the transaction)
J. transaction item sku
K. transaction item price
L. transaction item quantity
M. transaction item extended price
N. transaction item sales tax
O. transaction total
P. transaction payment type (cash, check, credit card)
Q. transaction payment amount
Following that, you might determine that these functional dependencies apply.
A->B
B->A
C->D
C->E
FH->GI
FH->O
J->K
FHJ->L
KL->M
KL->N
M->N
This is where you start normalizing. Each time you decompose one relation to raise it to a higher normal form, you add another relation. Each time you add another relation, you apply all the principles of normalization to it, too. The process is recursive.
Good CASE tools can generate every possible 5NF decomposition based on that list of dependencies.
Other design decisions are also important, but might have nothing to do with normalization.
For example, one design project might decide to store only one phone number per person. Another project might decide to store several phone numbers for each person. That kind of decision is important, but it has nothing to do with normalization. That is, storing one phone number per person doesn't violate any normal form, and neither does storing multiple phone numbers per person.