Important Concepts of DBMS

[karthick]

DBMS CONCEPTS


1. Database
A database is a logically coherent collection of data with some inherent meaning, representing some aspect of real world and which is designed, built and populated with data for a specific purpose
2. DBMS
It is a collection of programs that enables user to create and maintain a database. In other words it is general-purpose software that provides the users with the processes of defining, constructing and manipulating the database for various applications.

3. Database system
The database and DBMS software together is called as Database system.


4. Advantages of DBMS?

Redundancy is controlled.
Unauthorized access is restricted.
Ø providing multiple user interfaces.
Ø Enforcing integrity constraints.
Ø Providing backup and recovery.


5. Disadvantage in File Processing System

Ø Data redundancy & inconsistency.
Ø Difficult in accessing data.
Ø Data isolation.
Ø Data integrity.
Ø Concurrent access is not possible.
Ø Security Problems.


6.The three levels of data abstraction


Ø Physical level: The lowest level of abstraction describes how data are stored.

Ø Logical level: The next higher level of abstraction, describes what data are stored in database and what relationship among those data.

Ø View level: The highest level of abstraction describes only part of entire database.


11. Data Independence


Data independence means that "the application is independent of the storage structure and access strategy of data". In other words, The ability to modify the schema definition in one level should not affect the schema definition in the next higher level.

Two types of Data Independence:

Ø Physical Data Independence: Modification in physical level should not affect the logical level.
Ø Logical Data Independence: Modification in logical level should affect the view level.
NOTE: Logical Data Independence is more difficult to achieve


12. View & How is it related to data independence?

A view may be thought of as a virtual table, that is, a table that does not really exist in its own right but is instead derived from one or more underlying base table. In other words, there is no stored file that direct represents the view instead a definition of view is stored in data dictionary.
Growth and restructuring of base tables is not reflected in views. Thus the view can insulate users from the effects of restructuring and growth in the database. Hence accounts for logical data independence.


13 Data Model
A collection of conceptual tools for describing data, data relationships data semantics and constraints.


14. E-R model
This data model is based on real world that consists of basic objects called entities and of relationship among these objects. Entities are described in a database by a set of attributes.

15. Object Oriented model
This model is based on collection of objects. An object contains values stored in instance variables with in the object. An object also contains bodies of code that operate on the object. These bodies of code are called methods. Objects that contain same types of values and the same methods are grouped together into classes.

16 Entity
It is a 'thing' in the real world with an independent existence.


17. Entity type
It is a collection (set) of entities that have same attributes.


18. Entity set
It is a collection of all entities of particular entity type in the database.

19. Extension of entity type
The collections of entities of a particular entity type are grouped together into an entity set.

20.Weak Entity set
An entity set may not have sufficient attributes to form a primary key, and its primary key compromises of its partial key and primary key of its parent entity, then it is said to be Weak Entity set.

21.Attribute
It is a particular property, which describes the entity.

22 Relation Schema & Relation

A relation Schema denoted by R(A1, A2, ..., An) is made up of the relation name R and the list of attributes Ai that it contains. A relation is defined as a set of tuples. Let r be the relation which contains set tuples (t1, t2, t3, ..., tn). Each tuple is an ordered list of n-values t=(v1,v2, ..., vn).

23. Degree of a Relation
It is the number of attribute of its relation schema.


24. Relationship
It is an association among two or more entities.


25 Relationship set
The collection (or set) of similar relationships.

26. Relationship type
Relationship type defines a set of associations or a relationship set among a given set of entity types.

27. Degree of Relationship type
It is the number of entity type participating.

25. DDL (Data Definition Language)
A data base schema is specifies by a set of definitions expressed by a special language called DDL.

26. VDL (View Definition Language)
It specifies user views and their mappings to the conceptual schema.

29. DML (Data Manipulation Language)
This language that enable user to access or manipulate data as organised by appropriate data model.
Ø Procedural DML or Low level: DML requires a user to specify what data are needed and how to get those data.
Ø Non-Procedural DML or High level: DML requires a user to specify what data are needed without specifying how to get those data


30 Relational Algebra
It is procedural query language. It consists of a set of operations that take one or two relations as input and produce a new relation.

37. Relational Calculus
It is an applied predicate calculus specifically tailored for relational databases proposed by E.F. Codd.
E.g. of languages based on it are DSL ALPHA, QUEL.

38. Difference between Tuple-oriented relational calculus & domain-oriented relational calculus
The tuple-oriented calculus uses a tuple variables i.e., variable whose only permitted values are tuples of that relation. E.g. QUEL
The domain-oriented calculus has domain variables i.e., variables that range over the underlying domains instead of over relation. E.g. ILL, DEDUCE.

39. Normalization
It is a process of analysing the given relation schemas based on their Functional Dependencies (FDs) and primary key to achieve the properties
Ø Minimizing redundancy
Ø Minimizing insertion, deletion and update anomalies.

40. Functional Dependency
A Functional dependency is denoted by X Y between two sets of attributes X and Y that are subsets of R specifies a constraint on the possible tuple that can form a relation state r of R. The constraint is for any two tuples t1 and t2 in r if t1[X] = t2[X] then they have t1[Y] = t2[Y]. This means the value of X component of a tuple uniquely determines the value of component Y.

41. When is a functional dependency F said to be minimal?
Ø Every dependency in F has a single attribute for its right hand side.
Ø We cannot replace any dependency X A in F with a dependency Y A where Y is a proper subset of X and still have a set of dependency that is equivalent to F.
Ø We cannot remove any dependency from F and still have set of dependency that is equivalent to F.








42. Multivalued dependency

Multivalued dependency denoted by X Y specified on relation schema R, where X and Y are both subsets of R, specifies the following constraint on any relation r of R: if two tuples t1 and t2 exist in r such that t1[X] = t2[X] then t3 and t4 should also exist in r with the following properties
Ø t3

• = t4[X] = t1[X] = t2[X]
  Ø t3[Y] = t1[Y] and t4[Y] = t2[Y]
  Ø t3[Z] = t2[Z] and t4[Z] = t1[Z]
  where [Z = (R-(X U Y)) ]
  
  42 Lossless join property
  It guarantees that the spurious tuple generation does not occur with respect to relation schemas after decomposition.
  
  44. 1 NF (Normal Form)
  The domain of attribute must include only atomic (simple, indivisible) values.
  
  45. Fully Functional dependency
  It is based on concept of full functional dependency. A functional dependency X Y is full functional dependency if removal of any attribute A from X means that the dependency does not hold any more.
  
  46. 2NF
  A relation schema R is in 2NF if it is in 1NF and every non-prime attribute A in R is fully functionally dependent on primary key.
  
  47. 3NF
  
  A relation schema R is in 3NF if it is in 2NF and for every FD X A either of the following is true
  Ø X is a Super-key of R.
  Ø A is a prime attribute of R.
  In other words, if every non prime attribute is non-transitively dependent on primary key.
  
  48. BCNF (Boyce-Codd Normal Form)
  A relation schema R is in BCNF if it is in 3NF and satisfies an additional constraint that for every FD X A, X must be a candidate key.
  
  49. 4NF
  A relation schema R is said to be in 4NF if for every Multivalued dependency X Y that holds over R, one of following is true
  Ø X is subset or equal to (or) XY = R.
  Ø X is a super key.
  
  50. 5NF
  A Relation schema R is said to be 5NF if for every join dependency {R1, R2, ..., Rn} that holds R, one the following is true
  Ø Ri = R for some i.
  Ø The join dependency is implied by the set of FD, over R in which the left side is key of R.
  
  
  51. Atomicity and Aggregation
  Atomicity:
  Either all actions are carried out or none are. Users should not have to worry about the effect of incomplete transactions. DBMS ensures this by undoing the actions of incomplete transactions.
  Aggregation:
  A concept which is used to model a relationship between a collection of entities and relationships. It is used when we need to express a relationship among relationships.

[vidya gadhave]

DBMS CONCEPTS


1. Database
A database is a logically coherent collection of data with some inherent meaning, representing some aspect of real world and which is designed, built and populated with data for a specific purpose
2. DBMS
It is a collection of programs that enables user to create and maintain a database. In other words it is general-purpose software that provides the users with the processes of defining, constructing and manipulating the database for various applications.

3. Database system
The database and DBMS software together is called as Database system.


4. Advantages of DBMS?

Redundancy is controlled.
Unauthorized access is restricted.
Ø providing multiple user interfaces.
Ø Enforcing integrity constraints.
Ø Providing backup and recovery.


5. Disadvantage in File Processing System

Ø Data redundancy & inconsistency.
Ø Difficult in accessing data.
Ø Data isolation.
Ø Data integrity.
Ø Concurrent access is not possible.
Ø Security Problems.


6.The three levels of data abstraction


Ø Physical level: The lowest level of abstraction describes how data are stored.

Ø Logical level: The next higher level of abstraction, describes what data are stored in database and what relationship among those data.

Ø View level: The highest level of abstraction describes only part of entire database.


11. Data Independence


Data independence means that "the application is independent of the storage structure and access strategy of data". In other words, The ability to modify the schema definition in one level should not affect the schema definition in the next higher level.

Two types of Data Independence:

Ø Physical Data Independence: Modification in physical level should not affect the logical level.
Ø Logical Data Independence: Modification in logical level should affect the view level.
NOTE: Logical Data Independence is more difficult to achieve


12. View & How is it related to data independence?

A view may be thought of as a virtual table, that is, a table that does not really exist in its own right but is instead derived from one or more underlying base table. In other words, there is no stored file that direct represents the view instead a definition of view is stored in data dictionary.
Growth and restructuring of base tables is not reflected in views. Thus the view can insulate users from the effects of restructuring and growth in the database. Hence accounts for logical data independence.


13 Data Model
A collection of conceptual tools for describing data, data relationships data semantics and constraints.


14. E-R model
This data model is based on real world that consists of basic objects called entities and of relationship among these objects. Entities are described in a database by a set of attributes.

15. Object Oriented model
This model is based on collection of objects. An object contains values stored in instance variables with in the object. An object also contains bodies of code that operate on the object. These bodies of code are called methods. Objects that contain same types of values and the same methods are grouped together into classes.

16 Entity
It is a 'thing' in the real world with an independent existence.


17. Entity type
It is a collection (set) of entities that have same attributes.


18. Entity set
It is a collection of all entities of particular entity type in the database.

19. Extension of entity type
The collections of entities of a particular entity type are grouped together into an entity set.

20.Weak Entity set
An entity set may not have sufficient attributes to form a primary key, and its primary key compromises of its partial key and primary key of its parent entity, then it is said to be Weak Entity set.

21.Attribute
It is a particular property, which describes the entity.

22 Relation Schema & Relation

A relation Schema denoted by R(A1, A2, ..., An) is made up of the relation name R and the list of attributes Ai that it contains. A relation is defined as a set of tuples. Let r be the relation which contains set tuples (t1, t2, t3, ..., tn). Each tuple is an ordered list of n-values t=(v1,v2, ..., vn).

23. Degree of a Relation
It is the number of attribute of its relation schema.


24. Relationship
It is an association among two or more entities.


25 Relationship set
The collection (or set) of similar relationships.

26. Relationship type
Relationship type defines a set of associations or a relationship set among a given set of entity types.

27. Degree of Relationship type
It is the number of entity type participating.

25. DDL (Data Definition Language)
A data base schema is specifies by a set of definitions expressed by a special language called DDL.

26. VDL (View Definition Language)
It specifies user views and their mappings to the conceptual schema.

29. DML (Data Manipulation Language)
This language that enable user to access or manipulate data as organised by appropriate data model.
Ø Procedural DML or Low level: DML requires a user to specify what data are needed and how to get those data.
Ø Non-Procedural DML or High level: DML requires a user to specify what data are needed without specifying how to get those data


30 Relational Algebra
It is procedural query language. It consists of a set of operations that take one or two relations as input and produce a new relation.

37. Relational Calculus
It is an applied predicate calculus specifically tailored for relational databases proposed by E.F. Codd.
E.g. of languages based on it are DSL ALPHA, QUEL.

38. Difference between Tuple-oriented relational calculus & domain-oriented relational calculus
The tuple-oriented calculus uses a tuple variables i.e., variable whose only permitted values are tuples of that relation. E.g. QUEL
The domain-oriented calculus has domain variables i.e., variables that range over the underlying domains instead of over relation. E.g. ILL, DEDUCE.

39. Normalization
It is a process of analysing the given relation schemas based on their Functional Dependencies (FDs) and primary key to achieve the properties
Ø Minimizing redundancy
Ø Minimizing insertion, deletion and update anomalies.

40. Functional Dependency
A Functional dependency is denoted by X Y between two sets of attributes X and Y that are subsets of R specifies a constraint on the possible tuple that can form a relation state r of R. The constraint is for any two tuples t1 and t2 in r if t1[X] = t2[X] then they have t1[Y] = t2[Y]. This means the value of X component of a tuple uniquely determines the value of component Y.

41. When is a functional dependency F said to be minimal?
Ø Every dependency in F has a single attribute for its right hand side.
Ø We cannot replace any dependency X A in F with a dependency Y A where Y is a proper subset of X and still have a set of dependency that is equivalent to F.
Ø We cannot remove any dependency from F and still have set of dependency that is equivalent to F.








42. Multivalued dependency

Multivalued dependency denoted by X Y specified on relation schema R, where X and Y are both subsets of R, specifies the following constraint on any relation r of R: if two tuples t1 and t2 exist in r such that t1[X] = t2[X] then t3 and t4 should also exist in r with the following properties
Ø t3

• = t4[X] = t1[X] = t2[X]
  Ø t3[Y] = t1[Y] and t4[Y] = t2[Y]
  Ø t3[Z] = t2[Z] and t4[Z] = t1[Z]
  where [Z = (R-(X U Y)) ]
  
  42 Lossless join property
  It guarantees that the spurious tuple generation does not occur with respect to relation schemas after decomposition.
  
  44. 1 NF (Normal Form)
  The domain of attribute must include only atomic (simple, indivisible) values.
  
  45. Fully Functional dependency
  It is based on concept of full functional dependency. A functional dependency X Y is full functional dependency if removal of any attribute A from X means that the dependency does not hold any more.
  
  46. 2NF
  A relation schema R is in 2NF if it is in 1NF and every non-prime attribute A in R is fully functionally dependent on primary key.
  
  47. 3NF
  
  A relation schema R is in 3NF if it is in 2NF and for every FD X A either of the following is true
  Ø X is a Super-key of R.
  Ø A is a prime attribute of R.
  In other words, if every non prime attribute is non-transitively dependent on primary key.
  
  48. BCNF (Boyce-Codd Normal Form)
  A relation schema R is in BCNF if it is in 3NF and satisfies an additional constraint that for every FD X A, X must be a candidate key.
  
  49. 4NF
  A relation schema R is said to be in 4NF if for every Multivalued dependency X Y that holds over R, one of following is true
  Ø X is subset or equal to (or) XY = R.
  Ø X is a super key.
  
  50. 5NF
  A Relation schema R is said to be 5NF if for every join dependency {R1, R2, ..., Rn} that holds R, one the following is true
  Ø Ri = R for some i.
  Ø The join dependency is implied by the set of FD, over R in which the left side is key of R.
  
  
  51. Atomicity and Aggregation
  Atomicity:
  Either all actions are carried out or none are. Users should not have to worry about the effect of incomplete transactions. DBMS ensures this by undoing the actions of incomplete transactions.
  Aggregation:
  A concept which is used to model a relationship between a collection of entities and relationships. It is used when we need to express a relationship among relationships.