Entity Relationship Model

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Entity Relationship Model

  1. 1. diagram . Ms.V. Saranya AP/CSE Sri vidya college of Engg & Tech, Virudhunagar.
  2. 2. Objectives • To illustrate how relationships between entities are defined and refined. • To know how relationships are incorporated into the database design process. • To describe how ERD components affect database design and implementation
  3. 3. Topics • • • • • • • Design Process Modeling Constraints E-R Diagram Design Issues Weak Entity Sets Extended E-R Features
  4. 4. DATA unorganized form ex: student’s score
  5. 5. Information processed, structured and organized data ex: class average which can be calculated from data.
  6. 6. TABLE A table is a collection (rows) of data on a single related topic.
  7. 7. Difference between table and database Table Database A table is an object inside a database A database has tables of data, a table is a collection (rows) of data on a single related topic. A database can have 10 or thousands of tables Ex: employee table Contains only employees detail. But it not contains inventory detail. But DB is a collection of Employee table as well as inventory table.
  8. 8. Sample Table
  9. 9. Sample Database DB is a collection related tables
  10. 10. Why we need ER diagram  giving you image of how the tables should connect  what fields are going to be on each table  the tables connection, if many-tomany, one-to-many. “ER diagrams are easy for non-technical people to understand, and thus are typically used by database designers before the schema ever exists”
  11. 11. Entity • An entity is something that exists by itself. • Entity: Real-world object distinguishable from other objects. An entity is described using a set of attributes. ssn name Employees email
  12. 12. Examples of entities – Person: EMPLOYEE, STUDENT, PATIENT – Place: STORE, WAREHOUSE – Object: MACHINE, PRODUCT, CAR – Event: SALE,REGISTRATION, RENEWAL – Concept: ACCOUNT, COURSE
  13. 13. Entity set • Entity Set: A collection of similar entities. E.g., all employees. – – – All entities in an entity set have the same set of attributes. Each entity set has a key. Each attribute has a domain.
  14. 14. Example Association between the instances of one or more entity types EntityName Person, place, object, event or concept about which data is to be maintained Verb Phrase AttributeName named property or characteristic of an entity
  15. 15. RELATIONSHIP • Relationship: Association among two or more entities. e.g., rose works in Pharmacy department. • Relationship Set: Collection of similar relationships. • Same entity set could participate in different relationship sets, or in different “roles” in same set.
  16. 16. Relationship Example  Associations between instances of one or more entity types that is of interest  Given a name that describes its function. • relationship name is an active or a passive verb. Relationship name: writes Author Book An author writes one or more books A book can be written by one or more authors.
  17. 17. Degree of Relationships • Degree: number of entity types that participate in a relationship • Three cases – Unary: between two instances of one entity type – Binary: between the instances of two entity types – Ternary: among the instances of three entity types
  18. 18. Attributes • Example of entity types and associated attributes: STUDENT: Student_ID, Student_Name, Home_Address, Phone_Number, Major
  19. 19. Attribute types – Simple and composite attributes. – Single-valued and multi-valued attributes • Example: multivalued attribute: phone_numbers – Derived attributes • Can be computed from other attributes • Example: age, given date_of_birth
  20. 20. A composite attribute
  21. 21. Referential Attributes • Make Reference to another instance in another table Referential attribute: Ties the lecturer entity to another entity that is department. Name Ali 105 LG ali@a.com Mary 106 IT mary@a.com John 107 ENG john@a.com Lim Instance of Lecturer. IdNum DeptID Email 108 IT lim@a.com
  22. 22. Mapping Cardinality Constraints • Express the number of entities to which another entity can be associated via a relationship set. • Most useful in describing binary relationship sets. • For a binary relationship set the mapping cardinality must be one of the following types: – – – – One to one One to many Many to one Many to many
  23. 23. Mapping Cardinalities One to one One to many Note: Some elements in A and B may not be mapped to any elements in the other set
  24. 24. Mapping Cardinalities Many to one Many to many Note: Some elements in A and B may not be mapped to any elements in the other set
  25. 25. • Key and key attributes: KEY – Key: a unique value for an entity – Key attributes: a group of one or more attributes that uniquely identify an entity in the entity set • Super key, candidate key, and primary key – – Super key: a set of attributes that allows to identify and entity uniquely in the entity set Candidate key: minimal super key • – There can be many candidate keys Primary key: a candidate key chosen by the designer • Denoted by underlining in ER attributes.
  26. 26. Key Constraints • Consider Works_In: An employee can work in many departments; a dept can have many employees. • In contrast, each dept has at most one manager, according to the key constraint on Manages.
  27. 27. Weak Entity Sets • An entity set that does not have a primary key is referred to as a weak entity set. • The existence of a weak entity set depends on the existence of a identifying entity set – it must relate to the identifying entity set via a total, one-to-many relationship set from the identifying to the weak entity set – Identifying relationship depicted using a double diamond • The discriminator (or partial key) of a weak entity set is the set of attributes that distinguishes among all the entities of a weak entity set. • The primary key of a weak entity set is formed by the primary key of the strong entity set on which the weak entity set is existence dependent, plus the weak entity set’s discriminator.
  28. 28. • In a relational database, a Weak Entity is an entity that cannot be uniquely identified by its attributes alone; therefore, it must use a foreign key in conjunction with its attributes to create a primary key. The foreign key is typically a primary key of an entity it is related to.
  29. 29. Conceptual design • Conceptual design: (ER Model is used at this stage.) • Process of describing the data, relationships between the data, and the constraints on the data.
  30. 30. Entity-Relationship (ER) Diagram • ER Modeling is a “top-down” approach to database design. • Entity Relationship (ER) Diagram – A detailed, “logical representation” of the entities, associations and data elements for an organization or business Notation uses three main constructs – Data entities – Relationships – Attributes
  31. 31. E-R Diagrams  Rectangles represent entity sets.  Diamonds represent relationship sets.  Lines link attributes to entity sets and entity sets to relationship sets.  Ellipses represent attributes  Double ellipses represent multivalued attributes.  Dashed ellipses denote derived attributes.  Underline indicates primary key attributes (will study later)
  32. 32. E-R Diagram With Composite, Multivalued, and Derived Attributes
  33. 33. Relationship Sets with Attributes
  34. 34. Roles • Entity sets of a relationship need not be distinct • The labels “manager” and “worker” are called roles; they specify how employee entities interact via the works_for relationship set. • Roles are indicated in E-R diagrams by labeling the lines that connect diamonds to rectangles. • Role labels are optional, and are used to clarify semantics of the relationship
  35. 35. Cardinality and Connectivity • Relationships can be classified as either • one – to – one • one – to – many • many – to –many Connectivity • Cardinality : minimum and maximum number of instances of Entity B that can (or must be) associated with each instance of entity A.
  36. 36. Cardinality Constraints • We express cardinality constraints by drawing either a directed line ( ), signifying “one,” or an undirected line (—), signifying “many,” between the relationship set and the entity set. • One-to-one relationship: – A customer is associated with at most one loan via the relationship borrower – A loan is associated with at most one customer via borrower
  37. 37. One-To-Many Relationship • In the one-to-many relationship a loan is associated with at most one customer via borrower, a customer is associated with several (including 0) loans via borrower
  38. 38. Many-To-One Relationships • In a many-to-one relationship a loan is associated with several (including 0) customers via borrower, a customer is associated with at most one loan via borrower
  39. 39. Many-To-Many Relationship • A customer is associated with several (possibly 0) loans via borrower • A loan is associated with several (possibly 0) customers via borrower
  40. 40. Connectivity • Chen Model – 1 to represent one. – M to represent many 1 M • Crow’s Foot One many One or many Mandatory one , means (1,1)
  41. 41. Binary Relationships • 1:M relationship – Relational modeling ideal – Should be the norm in any relational database design The 1: M relationship between PAINTER and PAINTING
  42. 42. Binary Relationships • 1:1 relationship – Should be rare in any relational database design – A single entity instance in one entity class is related to a single entity instance in another entity class – Could indicate that two entities actually belong in the same table
  43. 43. The 1:1 Relationship Between PROFESSOR and DEPARTMENT
  44. 44. Binary Relationships • M:N relationships – Must be avoided because they lead to data redundancies. – Can be implemented by breaking it up to produce a set of 1:M relationships – Can avoid problems inherent to M:N relationship by creating a composite entity or bridge entity • This will be used to link the tables that were originally related in a M:N relationship • The composite entity structure includes-as foreign keys-at least the primary keys of the tables that are to be linked.
  45. 45. The M:N Relationship Between STUDENT and CLASS Bowser Smithson Accounting 1 (ACCT-211) Intro to Microcomputing (CIS-220) Intro to Statistics (QM-261) This CANNOT be implemented as shown next…..
  46. 46. Changing the M:N relationship to TWO 1:M relationships
  47. 47. Extended E-R • Specialization • Generalization • Aggregation
  48. 48. Specialization • Top-down design process: we designate sub groupings within an entity set that are distinctive from other entities in the set. • These sub groupings become lower-level entity sets that have attributes or participate in relationships that do not apply to the higherlevel entity set. • Depicted by a “triangle component labeled ISA” • Attribute inheritance – a lower-level entity set inherits all the attributes and relationship participation of the higher-level entity set to which it is linked.
  49. 49. Specialization Example
  50. 50. Generalization • A bottom-up design process – combine a number of entity sets that share the same features into a higher-level entity set. • Specialization and generalization are simple inversions of each other; they are represented in an E-R diagram in the same way. • The terms specialization and generalization are used interchangeably.
  51. 51. Specialization and Generalization (Cont.) • Can have multiple specializations of an entity set based on different features. • E.g. permanent_employee vs. temporary_employee, in addition to officer vs. secretary vs. teller • Each particular employee would be – a member of one of permanent_employee or temporary_employee, – and also a member of one of officer, secretary, or teller • The ISA relationship also referred to as “superclass – subclass” relationship
  52. 52. Design Constraints on a Specialization/Generalization • Constraint on which entities can be members of a given lower-level entity set. – Condition-defined : evaluated by an explicit condition or predicate. – User-defined : database user assigns • Constraint on whether or not entities may belong to more than one lower-level entity set within a single generalization. – Disjoint • An entity can belong to only one lower-level entity set • Noted in E-R diagram by writing disjoint next to the ISA triangle – Overlapping • an entity can belong to more than one lower-level entity set
  53. 53. Design Constraints on a Specialization/Generalization (Contd.) • Completeness constraint – Total : an entity must belong to one of the lower-level entity sets – Partial : an entity need not belong to one of the lower-level entity sets
  54. 54. Aggregation  Consider the ternary relationship works-on, which we saw earlier  Suppose we want to record managers for tasks performed by an employee at a branch
  55. 55. Aggregation (Cont.) • Relationship sets works_on and manages represent overlapping information – Every manages relationship corresponds to a works_on relationship – However, some works_on relationships may not correspond to any manages relationships • So we can’t discard the works_on relationship • Eliminate this redundancy via aggregation – Treat relationship as an abstract entity – Allows relationships between relationships – Abstraction of relationship into new entity • Without introducing redundancy, the following diagram represents: – An employee works on a particular job at a particular branch – An employee, branch, job combination may have an associated manager
  56. 56. Aggregation (Cont.) • Relationship sets works-on and represent overlapping information manages – Every manages relationship corresponds to a workson relationship – However, some works-on relationships may not correspond to any manages relationships  we can’t discard the works-on relationship • Redundancy problem  aggregation
  57. 57. E-R Diagram With Aggregation
  58. 58. Summary of Symbols Used in E-R Notation
  59. 59. Summary of Symbols (Cont.)
  60. 60. Alternative E-R Notations

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