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Chapter 6: Database Design Using the E- R Model
Outline ▪ ▪ ▪ ▪ ▪ ▪ Overview of the Design Process The Entity- Relationship Model Complex Attributes Mapping Cardinalities Primary Key Removing Redundant Attributes in Entity Sets ▪Reducing ER Diagrams to Relational Schemas ▪ ▪ ▪ Extended E-R Features Entity-Relationship Design Issues Alternative Notations for Modeling Data
Design Phases ▪ Initial phase -- characterize fully the data needs of the prospective database users. Second phase -- choosing a data model • Applying the concepts of the chosen data model • Translating these requirements into a conceptual schema of the database. • A fully developed conceptual schema indicates the functional requirements of the enterprise. ▪ Describe the kinds of operations (or transactions) that will be performed on the data. ▪
Design Phases (Cont.) ▪ Final Phase -- Moving from an abstract data model to the implementation of the database • Logical Design – Deciding on the database schema. ▪ Database design requires that we find a “good” collection of relation schemas. Business decision – What attributes should we record in the database? Computer Science decision – What relation schemas should we have and how should the attributes be distributed among the various relation schemas? ▪ ▪ • Physical Design – Deciding on the physical layout of the database
Design Alternatives ▪ In designing a database schema, we must ensure that we avoid two major pitfalls: • Redundancy: a bad design may result in repeat information. ▪ Redundant representation of information may lead to data inconsistency among the various copies of information • Incompleteness: a bad design may make certain aspects of the enterprise difficult or impossible to model. ▪ Avoiding bad designs is not enough. There may be a large number of good designs from which we must choose.
Design Approaches ▪ Entity Relationship Model (covered in this chapter) • Models an enterprise as a collection of entities and relationships ▪ Entity: a “thing” or “object” in the enterprise that is distinguishable from other objects • Described by a set of attributes ▪ Relationship: an association among several entities • Represented diagrammatically by an entity-relationship diagram: Normalization Theory (Chapter 7) • Formalize what designs are bad, and test for them ▪
Outline of the ER Model
ER model -- Database Modeling ▪ The ER data model was developed to facilitate database design by allowing specification of an enterprise schema that represents the overall logical structure of a database. The ER data model employs three basic concepts: • entity sets, • relationship sets, • attributes. The ER model also has an associated diagrammatic representation, the ER diagram, which can express the overall logical structure of a database graphically. ▪ ▪
Entity Sets ▪ An entity is an object that exists and is distinguishable from other objects. • Example: specific person, company, event, plant An entity set is a set of entities of the same type that share the same properties. • Example: set of all persons, companies, trees, holidays An entity is represented by a set of attributes; i.e., descriptive properties possessed by all members of an entity set. • Example: instructor = (ID, name, salary ) course= (course_id, title, credits) A subset of the attributes form a primary key of the entity set; i.e., uniquely identifying each member of the set. ▪ ▪ ▪
Entity Sets -- instructor and student
Representing Entity sets in ER Diagram ▪ Entity sets can be represented graphically as follows: • Rectangles represent entity sets. • Attributes listed inside entity rectangle • Underline indicates primary key attributes
Relationship Sets ▪ A relationship is an association among several entities Example: 44553 (Peltier) advisor 22222 (Einstein) student entityrelationship set instructor entity ▪ A relationship set is a mathematical relation among n ; 2 entities, each taken from entity sets {(e1, e2, … en) | e1  E1, e2  E2, …, en  En} where (e1, e2, …, en) is a relationship • Example: (44553,22222)  advisor
Relationship Sets (Cont.) ▪ Example: we define the relationship set advisor to denote the associations between students and the instructors who act as their advisors. Pictorially, we draw a line between related entities. ▪
Representing Relationship Sets via ER Diagrams ▪ Diamonds represent relationship sets.
Relationship Sets (Cont.) ▪ ▪ An attribute can also be associated with a relationship set. For instance, the advisor relationship set between entity sets instructor and student may have the attribute date which tracks when the student started being associated with the advisor instructor student 76766 Crick Katz Srinivasan Kim Singh Einstein 45565 10101 98345 76543 22222 98988 12345 00128 76543 44553 Tanaka Shankar Zhang Brown Aoi Chavez Peltier 3 May 2008 10 June 2007 12 June 2006 6 June 2009 30 June 2007 31 May 2007 4 May 2006 76653 23121
Relationship Sets with Attributes
Roles ▪ Entity sets of a relationship need not be distinct •Each occurrence of an entity set plays a “role” in the relationship The labels “course_id” and “prereq_id” are called roles. ▪
Degree of a Relationship Set ▪ Binary relationship • involve two entity sets (or degree two). • most relationship sets in a database system are binary. Relationships between more than two entity sets are rare. Most relationships are binary. • Example: students work on research projects under the guidance of an instructor. • relationship proj_guide is a ternary relationship between instructor, student, and project ▪
Non-binary Relationship Sets  Most relationship sets are binary  There are occasions when it is more convenient to represent relationships as non-binary.  A simultaneous relationship between the instances of three entity types with unique attributes is called a ternary relationship.  E-R Diagram with a Ternary Relationship
E-R Diagram with a Ternary Relationship
Complex Attributes ▪ Attribute types: • Simple and composite attributes. • Single-valued and multivalued attributes ▪ Example: multivalued attribute: phone_numbers • Derived attributes ▪ ▪ Can be computed from other attributes Example: age, given date_of_birth ▪ Domain – the set of permitted values for each attribute
Composite Attributes ▪ Composite attributes allow us to divided attributes into subparts (other attributes). name address first_name middle_initial last_name street city state postal_code street_number street_name apartment_number composite attributes component attributes
Representing Complex Attributes in ER Diagram
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 ▪ ▪
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
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
Representing Cardinality Constraints in ER Diagram ▪ 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 between an instructor and a student : • A student is associated with at most one instructor via the relationship advisor • A student is associated with at most one department via stud_dept ▪ 6.27
One-to-Many Relationship ▪ A one-to-many relationship between an instructor and a student • A student is associated with several (including 0) instructors via advisor • A student is associated with at least one instructor via an advisor,
Many-to-One Relationships ▪ In a many-to-one relationship between an instructor and a student, • An instructor is associated with at least one student via an advisor, • And a student is associated with at most one (including 0) instructors via advisor
Many-to-Many Relationship ▪ ▪ An instructor is associated with several (possibly 0) students via advisor A student is associated with several (possibly 0) instructors via advisor
Total and Partial Participation ▪ Total participation (indicated by double line): every entity in the entity set participates in at least one relationship in the relationship set participation of student in advisor relation is total ▪ every student must have an associated instructor ▪ Partial participation: some entities may not participate in any relationship in the relationship set • Example: participation of instructor in advisor is partial
Notation for Expressing More Complex Constraints ▪ A line may have an associated minimum and maximum cardinality, shown in the form l..h, where l is the minimum and h the maximum cardinality • A minimum value of 1 indicates total participation. • A maximum value of 1 indicates that the entity participates in at most one relationship • A maximum value of * indicates no limit. ▪ Example • Instructor can advise 0 or more students. A student must have 1 advisor; cannot have multiple advisors
Cardinality Constraints on Ternary Relationship ▪ We allow at most one arrow out of a ternary (or greater degree) relationship to indicate a cardinality constraint For example, an arrow from proj_guide to instructor indicates each student has at most one guide for a project If there is more than one arrow, there are two ways of defining the meaning. • For example, a ternary relationship R between A, B and C with arrows to B and C could mean 1. Each A entity is associated with a unique entity from B and C or ▪ ▪ 2. Each pair of entities from (A, B) is associated with a unique C entity, and each pair (A, C) is associated with a unique B • Each alternative has been used in different formalisms • To avoid confusion we outlaw more than one arrow
6.34 Primary Key ▪ Primary keys provide a way to specify how entities and relations are distinguished. We will consider: • Entity sets • Relationship sets. • Weak entity sets
Primary key for Entity Sets ▪ ▪ By definition, individual entities are distinct. From database perspective, the differences among them must be expressed in terms of their attributes. The values of the attribute values of an entity must be such that they can uniquely identify the entity. • No two entities in an entity set are allowed to have exactly the same value for all attributes. A key for an entity is a set of attributes that suffice to distinguish entities from each other ▪ ▪
Primary Key for Relationship Sets ▪ To distinguish among the various relationships of a relationship set we use the individual primary keys of the entities in the relationship set. • Let R be a relationship set involving entity sets E1, E2, .. En • The primary key for R is consists of the union of the primary keys of entity sets E1, E2, ..En • If the relationship set R has attributes a1, a2, .., am associated with it, then the primary key of R also includes the attributes a1, a2, .., am Example: relationship set “advisor”. • The primary key consists of instructor.ID and student.ID The choice of the primary key for a relationship set depends on the mapping cardinality of the relationship set. ▪ ▪
Choice of Primary key for Binary Relationship ▪ Many-to-Many relationships.The preceding union of the primary keys is a minimal superkey and is chosen as the primary key. ▪ One-to-Many relationships . The primary key of the “Many” side is a minimal superkey and is used as the primary key. Many-to-one relationships. The primary key of the “Many” side is a minimal superkey and is used as the primary key. One-to-one relationships. The primary key of either one of the participating entity sets forms a minimal superkey, and either one can be chosen as the primary key. ▪ ▪
Weak Entity Sets ▪ Consider a section entity, which is uniquely identified by a course_id, semester, year, and sec_id. Clearly, section entities are related to course entities. Suppose we create a relationship set sec_course between entity sets section and course. Note that the information in sec_course is redundant, since section already has an attribute course_id, which identifies the course with which the section is related. One option to deal with this redundancy is to get rid of the relationship sec_course; however, by doing so the relationship between section and course becomes implicit in an attribute, which is not desirable. ▪ ▪ ▪
Weak Entity Sets (Cont.) ▪ An alternative way to deal with this redundancy is to not store the attribute course_id in the section entity and to only store the remaining attributes section_id, year, and semester. • However, the entity set section then does not have enough attributes to identify a particular section entity uniquely To deal with this problem, we treat the relationship sec_course as a special relationship that provides extra information, in this case, the course_id, required to identify section entities uniquely. A weak entity set is one whose existence is dependent on another entity, called its identifying entity Instead of associating a primary key with a weak entity, we use the identifying entity, along with extra attributes called discriminator to uniquely identify a weak entity. ▪ ▪ ▪
Weak Entity Sets (Cont.) 6.40 ▪ ▪ An entity set that is not a weak entity set is termed a strong entity set. Every weak entity must be associated with an identifying entity; that is, the weak entity set is said to be existence dependent on the identifying entity set. The identifying entity set is said to own the weak entity set that it identifies. The relationship associating the weak entity set with the identifying entity set is called the identifying relationship. Note that the relational schema we eventually create from the entity set section does have the attribute course_id, for reasons that will become clear later, even though we have dropped the attribute course_id from the entity set section. ▪ ▪ ▪
Expressing Weak Entity Sets ▪ ▪ ▪ In E-R diagrams, a weak entity set is depicted via a double rectangle. We underline the discriminator of a weak entity set with a dashed line. The relationship set connecting the weak entity set to the identifying strong entity set is depicted by a double diamond. Primary key for section – (course_id, sec_id, semester, year) ▪
Redundant Attributes ▪ Suppose we have entity sets: • student, with attributes: ID, name, tot_cred, dept_name • department, with attributes: dept_name, building, budget We model the fact that each student has an associated department using a relationship set stud_dept The attribute dept_name in student below replicates information present in the relationship and is therefore redundant • and needs to be removed. BUT: when converting back to tables, in some cases the attribute gets reintroduced, as we will see later. ▪ ▪ ▪
E-R Diagram for a University Enterprise 6.43
Reduction to Relation Schemas
Reduction to Relation Schemas ▪ Entity sets and relationship sets can be expressed uniformly as relation schemas that represent the contents of the database. A database which conforms to an E-R diagram can be represented by a collection of schemas. For each entity set and relationship set there is a unique schema that is assigned the name of the corresponding entity set or relationship set. Each schema has a number of columns (generally corresponding to attributes), which have unique names. ▪ ▪ ▪
Representing Entity Sets ▪ A strong entity set reduces to a schema with the same attributes student(ID, name, tot_cred) A weak entity set becomes a table that includes a column for the primary key of the identifying strong entity set section ( course_id, sec_id, sem, year ) Example ▪ ▪
Representation of Entity Sets with Composite Attributes ▪ Composite attributes are flattened out by creating a separate attribute for each component attribute • Example: given entity set instructor with composite attribute name with component attributes first_name and last_name the schema corresponding to the entity set has two attributes name_first_name and name_last_name ▪ Prefix omitted if there is no ambiguity (name_first_name could be first_name) Ignoring multivalued attributes, extended instructor schema is • instructor(ID, first_name, middle_initial, last_name, street_number, street_name, apt_number, city, state, zip_code, date_of_birth) ▪
Representation of Entity Sets with Multivalued Attributes ▪ A multivalued attribute M of an entity E is represented by a separate schema EM Schema EM has attributes corresponding to the primary key of E and an attribute corresponding to multivalued attribute M Example: Multivalued attribute phone_number of instructor is represented by a schema: inst_phone= ( ID, phone_number) Each value of the multivalued attribute maps to a separate tuple of the relation on schema EM • For example, an instructor entity with primary key 22222 and phone numbers 456-7890 and 123-4567 maps to two tuples: (22222, 456-7890) and (22222, 123-4567) ▪ ▪ ▪
Representing Relationship Sets ▪ A many-to-many relationship set is represented as a schema with attributes for the primary keys of the two participating entity sets, and any descriptive attributes of the relationship set. Example: schema for relationship set advisor ▪ advisor = (s_id, i_id)
Redundancy of Schemas ▪ Many-to-one and one-to-many relationship sets that are total on the many- side can be represented by adding an extra attribute to the “many” side, containing the primary key of the “one” side Example: Instead of creating a schema for relationship set inst_dept, add an attribute dept_name to the schema arising from entity set instructor Example ▪ ▪
Redundancy of Schemas (Cont.) ▪ For one-to-one relationship sets, either side can be chosen to act as the “many” side • That is, an extra attribute can be added to either of the tables corresponding to the two entity sets If participation is partial on the “many” side, replacing a schema by an extra attribute in the schema corresponding to the “many” side could result in null values ▪
Redundancy of Schemas (Cont.) ▪ The schema corresponding to a relationship set linking a weak entity set to its identifying strong entity set is redundant. Example: The section schema already contains the attributes that would appear in the sec_course schema ▪
Extended E-R Features
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 higher-level entity set. Depicted by a triangle component labeled ISA (e.g., instructor “is a” person). 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. ▪ ▪ ▪
Specialization Example ▪ ▪ ▪ Overlapping – employee and student Disjoint – instructor and secretary Total and partial
Representing Specialization via Schemas ▪ Method 1: • Form a schema for the higher-level entity • Form a schema for each lower-level entity set, include primary key of higher-level entity set and local attributes • Drawback: getting information about, an employee requires accessing two relations, the one corresponding to the low-level schema and the one corresponding to the high-level schema
Representing Specialization as Schemas (Cont.) ▪ Method 2: • Form a schema for each entity set with all local and inherited attributes • Drawback: name, street and city may be stored redundantly for people who are both students and employees
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. ▪ ▪
Completeness constraint ▪ Completeness constraint -- specifies whether or not an entity in the higher-level entity set must belong to at least one of the lower- level entity sets within a generalization. • 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
Completeness constraint (Cont.) ▪ ▪ Partial generalization is the default. We can specify total generalization in an ER diagram by adding the keyword total in the diagram and drawing a dashed line from the keyword to the corresponding hollow arrow-head to which it applies (for a total generalization), or to the set of hollow arrow- heads to which it applies (for an overlapping generalization). The student generalization is total: All student entities must be either graduate or undergraduate. Because the higher-level entity set arrived at through generalization is generally composed of only those entities in the lower-level entity sets, the completeness constraint for a generalized higher-level entity set is usually total ▪
Aggregation ▪ ▪ Consider the ternary relationship proj_guide, which we saw earlier Suppose we want to record evaluations of a student by a guide on a project
Aggregation (Cont.) ▪ Relationship sets eval_for and proj_guide represent overlapping information • Every eval_for relationship corresponds to a proj_guide relationship • However, some proj_guide relationships may not correspond to any eval_for relationships ▪So we can’t discard the proj_guide relationship Eliminate this redundancy via aggregation • Treat relationship as an abstract entity • Allows relationships between relationships • Abstraction of relationship into new entity ▪
Aggregation (Cont.) ▪ Eliminate this redundancy via aggregation without introducing redundancy, the following diagram represents: • A student is guided by a particular instructor on a particular project • A student, instructor, project combination may have an associated evaluation
Reduction to Relational Schemas ▪ To represent aggregation, create a schema containing • Primary key of the aggregated relationship, • The primary key of the associated entity set •Any descriptive attributes In our example: ▪ • The schema eval_for is: eval_for (s_ID, project_id, i_ID, evaluation_id) • The schema proj_guide is redundant.
Design Issues
Common Mistakes in E-R Diagrams ▪ Example of erroneous E-R diagrams
Common Mistakes in E-R Diagrams (Cont.)
Entities vs. Attributes ▪ Use of entity sets vs. attributes ▪ Use of phone as an entity allows extra information about phone numbers (plus multiple phone numbers)
Entities vs. Relationship sets ▪ Use of entity sets vs. relationship sets Possible guideline is to designate a relationship set to describe an action that occurs between entities ▪ Placement of relationship attributes For example, attribute date as attribute of advisor or as attribute of student
Binary Vs. Non-Binary Relationships ▪ Although it is possible to replace any non-binary (n-ary, for n > 2) relationship set by a number of distinct binary relationship sets, a n-ary relationship set shows more clearly that several entities participate in a single relationship. Some relationships that appear to be non-binary may be better represented using binary relationships • For example, a ternary relationship parents, relating a child to his/her father and mother, is best replaced by two binary relationships, father and mother ▪ ▪ Using two binary relationships allows partial information (e.g., only mother being known) • But there are some relationships that are naturally non- binary ▪ Example: proj_guide
Converting Non-Binary Relationships to Binary Form ▪ In general, any non-binary relationship can be represented using binary relationships by creating an artificial entity set. • Replace R between entity sets A, B and C by an entity set E, and three relationship sets: 1. RA, relating E and A 2. RB, relating E and B 3. RC, relating E and C • Create an identifying attribute for E and add any attributes of R to E • For each relationship (ai , bi , ci) in R, create 1. a new entity ei in the entity set E 2. add (ei , ai ) to RA 3. add (ei , bi ) to RB 4. add (ei , ci ) to RC
Converting Non-Binary Relationships (Cont.) ▪ Also need to translate constraints • Translating all constraints may not be possible • There may be instances in the translated schema that cannot correspond to any instance of R ▪ Exercise: add constraints to the relationships RA, RB and RC to ensure that a newly created entity corresponds to exactly one entity in each of entity sets A, B and C • We can avoid creating an identifying attribute by making E a weak entity set (described shortly) identified by the three relationship sets
E-R Design Decisions ▪ ▪ The use of an attribute or entity set to represent an object. Whether a real-world concept is best expressed by an entity set or a relationship set. The use of a ternary relationship versus a pair of binary relationships. The use of a strong or weak entity set. The use of specialization/generalization – contributes to modularity in the design. The use of aggregation – can treat the aggregate entity set as a single unit without concern for the details of its internal structure. ▪ ▪ ▪ ▪
Summary of Symbols Used in E-R Notation
Symbols Used in E-R Notation (Cont.)
Alternative ER Notations ▪ Chen, IDE1FX, …
Alternative ER Notations Chen IDE1FX (Crows feet notation)
UML ▪ ▪ UML: Unified Modeling Language UML has many components to graphically model different aspects of an entire software system UML Class Diagrams correspond to E-R Diagram, but several differences. ▪
ER vs. UML Class Diagrams * Note reversal of position in cardinality constraint depiction
ER vs. UML Class Diagrams ER Diagram Notation Equivalent in UML * Generalization can use merged or separate arrows independent of disjoint/overlapping
UML Class Diagrams (Cont.) ▪ Binary relationship sets are represented in UML by just drawing a line connecting the entity sets. The relationship set name is written adjacent to the line. The role played by an entity set in a relationship set may also be specified by writing the role name on the line, adjacent to the entity set. The relationship set name may alternatively be written in a box, along with attributes of the relationship set, and the box is connected, using a dotted line, to the line depicting the relationship set. ▪ ▪
ER vs. UML Class Diagrams
Other Aspects of Database Design Database System Concepts - 7th 6.83 ©Silberschatz, Korth and ▪ ▪ ▪ Functional Requirements Data Flow, Workflow Schema Evolution
End of Chapter 6 Database System Concepts - 7th 6.84 ©Silberschatz, Korth and

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Database design .pptx PPTS unit-I PPT Notes of DBMS

  • 1.
    Chapter 6: DatabaseDesign Using the E- R Model
  • 2.
    Outline ▪ ▪ ▪ ▪ ▪ ▪ Overview of theDesign Process The Entity- Relationship Model Complex Attributes Mapping Cardinalities Primary Key Removing Redundant Attributes in Entity Sets ▪Reducing ER Diagrams to Relational Schemas ▪ ▪ ▪ Extended E-R Features Entity-Relationship Design Issues Alternative Notations for Modeling Data
  • 3.
    Design Phases ▪ Initialphase -- characterize fully the data needs of the prospective database users. Second phase -- choosing a data model • Applying the concepts of the chosen data model • Translating these requirements into a conceptual schema of the database. • A fully developed conceptual schema indicates the functional requirements of the enterprise. ▪ Describe the kinds of operations (or transactions) that will be performed on the data. ▪
  • 4.
    Design Phases (Cont.) ▪Final Phase -- Moving from an abstract data model to the implementation of the database • Logical Design – Deciding on the database schema. ▪ Database design requires that we find a “good” collection of relation schemas. Business decision – What attributes should we record in the database? Computer Science decision – What relation schemas should we have and how should the attributes be distributed among the various relation schemas? ▪ ▪ • Physical Design – Deciding on the physical layout of the database
  • 5.
    Design Alternatives ▪ Indesigning a database schema, we must ensure that we avoid two major pitfalls: • Redundancy: a bad design may result in repeat information. ▪ Redundant representation of information may lead to data inconsistency among the various copies of information • Incompleteness: a bad design may make certain aspects of the enterprise difficult or impossible to model. ▪ Avoiding bad designs is not enough. There may be a large number of good designs from which we must choose.
  • 6.
    Design Approaches ▪ EntityRelationship Model (covered in this chapter) • Models an enterprise as a collection of entities and relationships ▪ Entity: a “thing” or “object” in the enterprise that is distinguishable from other objects • Described by a set of attributes ▪ Relationship: an association among several entities • Represented diagrammatically by an entity-relationship diagram: Normalization Theory (Chapter 7) • Formalize what designs are bad, and test for them ▪
  • 7.
  • 8.
    ER model --Database Modeling ▪ The ER data model was developed to facilitate database design by allowing specification of an enterprise schema that represents the overall logical structure of a database. The ER data model employs three basic concepts: • entity sets, • relationship sets, • attributes. The ER model also has an associated diagrammatic representation, the ER diagram, which can express the overall logical structure of a database graphically. ▪ ▪
  • 9.
    Entity Sets ▪ Anentity is an object that exists and is distinguishable from other objects. • Example: specific person, company, event, plant An entity set is a set of entities of the same type that share the same properties. • Example: set of all persons, companies, trees, holidays An entity is represented by a set of attributes; i.e., descriptive properties possessed by all members of an entity set. • Example: instructor = (ID, name, salary ) course= (course_id, title, credits) A subset of the attributes form a primary key of the entity set; i.e., uniquely identifying each member of the set. ▪ ▪ ▪
  • 10.
    Entity Sets --instructor and student
  • 11.
    Representing Entity setsin ER Diagram ▪ Entity sets can be represented graphically as follows: • Rectangles represent entity sets. • Attributes listed inside entity rectangle • Underline indicates primary key attributes
  • 12.
    Relationship Sets ▪ Arelationship is an association among several entities Example: 44553 (Peltier) advisor 22222 (Einstein) student entityrelationship set instructor entity ▪ A relationship set is a mathematical relation among n ; 2 entities, each taken from entity sets {(e1, e2, … en) | e1  E1, e2  E2, …, en  En} where (e1, e2, …, en) is a relationship • Example: (44553,22222)  advisor
  • 13.
    Relationship Sets (Cont.) ▪Example: we define the relationship set advisor to denote the associations between students and the instructors who act as their advisors. Pictorially, we draw a line between related entities. ▪
  • 14.
    Representing Relationship Setsvia ER Diagrams ▪ Diamonds represent relationship sets.
  • 15.
    Relationship Sets (Cont.) ▪ ▪ Anattribute can also be associated with a relationship set. For instance, the advisor relationship set between entity sets instructor and student may have the attribute date which tracks when the student started being associated with the advisor instructor student 76766 Crick Katz Srinivasan Kim Singh Einstein 45565 10101 98345 76543 22222 98988 12345 00128 76543 44553 Tanaka Shankar Zhang Brown Aoi Chavez Peltier 3 May 2008 10 June 2007 12 June 2006 6 June 2009 30 June 2007 31 May 2007 4 May 2006 76653 23121
  • 16.
  • 17.
    Roles ▪ Entity setsof a relationship need not be distinct •Each occurrence of an entity set plays a “role” in the relationship The labels “course_id” and “prereq_id” are called roles. ▪
  • 18.
    Degree of aRelationship Set ▪ Binary relationship • involve two entity sets (or degree two). • most relationship sets in a database system are binary. Relationships between more than two entity sets are rare. Most relationships are binary. • Example: students work on research projects under the guidance of an instructor. • relationship proj_guide is a ternary relationship between instructor, student, and project ▪
  • 19.
    Non-binary Relationship Sets Most relationship sets are binary  There are occasions when it is more convenient to represent relationships as non-binary.  A simultaneous relationship between the instances of three entity types with unique attributes is called a ternary relationship.  E-R Diagram with a Ternary Relationship
  • 20.
    E-R Diagram witha Ternary Relationship
  • 21.
    Complex Attributes ▪ Attributetypes: • Simple and composite attributes. • Single-valued and multivalued attributes ▪ Example: multivalued attribute: phone_numbers • Derived attributes ▪ ▪ Can be computed from other attributes Example: age, given date_of_birth ▪ Domain – the set of permitted values for each attribute
  • 22.
    Composite Attributes ▪ Compositeattributes allow us to divided attributes into subparts (other attributes). name address first_name middle_initial last_name street city state postal_code street_number street_name apartment_number composite attributes component attributes
  • 23.
  • 24.
    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 ▪ ▪
  • 25.
    Mapping Cardinalities One to one Oneto many Note: Some elements in A and B may not be mapped to any elements in the other set
  • 26.
    Mapping Cardinalities Many to one Manyto many Note: Some elements in A and B may not be mapped to any elements in the other set
  • 27.
    Representing Cardinality Constraintsin ER Diagram ▪ 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 between an instructor and a student : • A student is associated with at most one instructor via the relationship advisor • A student is associated with at most one department via stud_dept ▪ 6.27
  • 28.
    One-to-Many Relationship ▪ Aone-to-many relationship between an instructor and a student • A student is associated with several (including 0) instructors via advisor • A student is associated with at least one instructor via an advisor,
  • 29.
    Many-to-One Relationships ▪ Ina many-to-one relationship between an instructor and a student, • An instructor is associated with at least one student via an advisor, • And a student is associated with at most one (including 0) instructors via advisor
  • 30.
    Many-to-Many Relationship ▪ ▪ An instructoris associated with several (possibly 0) students via advisor A student is associated with several (possibly 0) instructors via advisor
  • 31.
    Total and PartialParticipation ▪ Total participation (indicated by double line): every entity in the entity set participates in at least one relationship in the relationship set participation of student in advisor relation is total ▪ every student must have an associated instructor ▪ Partial participation: some entities may not participate in any relationship in the relationship set • Example: participation of instructor in advisor is partial
  • 32.
    Notation for ExpressingMore Complex Constraints ▪ A line may have an associated minimum and maximum cardinality, shown in the form l..h, where l is the minimum and h the maximum cardinality • A minimum value of 1 indicates total participation. • A maximum value of 1 indicates that the entity participates in at most one relationship • A maximum value of * indicates no limit. ▪ Example • Instructor can advise 0 or more students. A student must have 1 advisor; cannot have multiple advisors
  • 33.
    Cardinality Constraints onTernary Relationship ▪ We allow at most one arrow out of a ternary (or greater degree) relationship to indicate a cardinality constraint For example, an arrow from proj_guide to instructor indicates each student has at most one guide for a project If there is more than one arrow, there are two ways of defining the meaning. • For example, a ternary relationship R between A, B and C with arrows to B and C could mean 1. Each A entity is associated with a unique entity from B and C or ▪ ▪ 2. Each pair of entities from (A, B) is associated with a unique C entity, and each pair (A, C) is associated with a unique B • Each alternative has been used in different formalisms • To avoid confusion we outlaw more than one arrow
  • 34.
    6.34 Primary Key ▪ Primarykeys provide a way to specify how entities and relations are distinguished. We will consider: • Entity sets • Relationship sets. • Weak entity sets
  • 35.
    Primary key forEntity Sets ▪ ▪ By definition, individual entities are distinct. From database perspective, the differences among them must be expressed in terms of their attributes. The values of the attribute values of an entity must be such that they can uniquely identify the entity. • No two entities in an entity set are allowed to have exactly the same value for all attributes. A key for an entity is a set of attributes that suffice to distinguish entities from each other ▪ ▪
  • 36.
    Primary Key forRelationship Sets ▪ To distinguish among the various relationships of a relationship set we use the individual primary keys of the entities in the relationship set. • Let R be a relationship set involving entity sets E1, E2, .. En • The primary key for R is consists of the union of the primary keys of entity sets E1, E2, ..En • If the relationship set R has attributes a1, a2, .., am associated with it, then the primary key of R also includes the attributes a1, a2, .., am Example: relationship set “advisor”. • The primary key consists of instructor.ID and student.ID The choice of the primary key for a relationship set depends on the mapping cardinality of the relationship set. ▪ ▪
  • 37.
    Choice of Primarykey for Binary Relationship ▪ Many-to-Many relationships.The preceding union of the primary keys is a minimal superkey and is chosen as the primary key. ▪ One-to-Many relationships . The primary key of the “Many” side is a minimal superkey and is used as the primary key. Many-to-one relationships. The primary key of the “Many” side is a minimal superkey and is used as the primary key. One-to-one relationships. The primary key of either one of the participating entity sets forms a minimal superkey, and either one can be chosen as the primary key. ▪ ▪
  • 38.
    Weak Entity Sets ▪Consider a section entity, which is uniquely identified by a course_id, semester, year, and sec_id. Clearly, section entities are related to course entities. Suppose we create a relationship set sec_course between entity sets section and course. Note that the information in sec_course is redundant, since section already has an attribute course_id, which identifies the course with which the section is related. One option to deal with this redundancy is to get rid of the relationship sec_course; however, by doing so the relationship between section and course becomes implicit in an attribute, which is not desirable. ▪ ▪ ▪
  • 39.
    Weak Entity Sets(Cont.) ▪ An alternative way to deal with this redundancy is to not store the attribute course_id in the section entity and to only store the remaining attributes section_id, year, and semester. • However, the entity set section then does not have enough attributes to identify a particular section entity uniquely To deal with this problem, we treat the relationship sec_course as a special relationship that provides extra information, in this case, the course_id, required to identify section entities uniquely. A weak entity set is one whose existence is dependent on another entity, called its identifying entity Instead of associating a primary key with a weak entity, we use the identifying entity, along with extra attributes called discriminator to uniquely identify a weak entity. ▪ ▪ ▪
  • 40.
    Weak Entity Sets(Cont.) 6.40 ▪ ▪ An entity set that is not a weak entity set is termed a strong entity set. Every weak entity must be associated with an identifying entity; that is, the weak entity set is said to be existence dependent on the identifying entity set. The identifying entity set is said to own the weak entity set that it identifies. The relationship associating the weak entity set with the identifying entity set is called the identifying relationship. Note that the relational schema we eventually create from the entity set section does have the attribute course_id, for reasons that will become clear later, even though we have dropped the attribute course_id from the entity set section. ▪ ▪ ▪
  • 41.
    Expressing Weak EntitySets ▪ ▪ ▪ In E-R diagrams, a weak entity set is depicted via a double rectangle. We underline the discriminator of a weak entity set with a dashed line. The relationship set connecting the weak entity set to the identifying strong entity set is depicted by a double diamond. Primary key for section – (course_id, sec_id, semester, year) ▪
  • 42.
    Redundant Attributes ▪ Supposewe have entity sets: • student, with attributes: ID, name, tot_cred, dept_name • department, with attributes: dept_name, building, budget We model the fact that each student has an associated department using a relationship set stud_dept The attribute dept_name in student below replicates information present in the relationship and is therefore redundant • and needs to be removed. BUT: when converting back to tables, in some cases the attribute gets reintroduced, as we will see later. ▪ ▪ ▪
  • 43.
    E-R Diagram fora University Enterprise 6.43
  • 44.
  • 45.
    Reduction to RelationSchemas ▪ Entity sets and relationship sets can be expressed uniformly as relation schemas that represent the contents of the database. A database which conforms to an E-R diagram can be represented by a collection of schemas. For each entity set and relationship set there is a unique schema that is assigned the name of the corresponding entity set or relationship set. Each schema has a number of columns (generally corresponding to attributes), which have unique names. ▪ ▪ ▪
  • 46.
    Representing Entity Sets ▪A strong entity set reduces to a schema with the same attributes student(ID, name, tot_cred) A weak entity set becomes a table that includes a column for the primary key of the identifying strong entity set section ( course_id, sec_id, sem, year ) Example ▪ ▪
  • 47.
    Representation of EntitySets with Composite Attributes ▪ Composite attributes are flattened out by creating a separate attribute for each component attribute • Example: given entity set instructor with composite attribute name with component attributes first_name and last_name the schema corresponding to the entity set has two attributes name_first_name and name_last_name ▪ Prefix omitted if there is no ambiguity (name_first_name could be first_name) Ignoring multivalued attributes, extended instructor schema is • instructor(ID, first_name, middle_initial, last_name, street_number, street_name, apt_number, city, state, zip_code, date_of_birth) ▪
  • 48.
    Representation of EntitySets with Multivalued Attributes ▪ A multivalued attribute M of an entity E is represented by a separate schema EM Schema EM has attributes corresponding to the primary key of E and an attribute corresponding to multivalued attribute M Example: Multivalued attribute phone_number of instructor is represented by a schema: inst_phone= ( ID, phone_number) Each value of the multivalued attribute maps to a separate tuple of the relation on schema EM • For example, an instructor entity with primary key 22222 and phone numbers 456-7890 and 123-4567 maps to two tuples: (22222, 456-7890) and (22222, 123-4567) ▪ ▪ ▪
  • 49.
    Representing Relationship Sets ▪A many-to-many relationship set is represented as a schema with attributes for the primary keys of the two participating entity sets, and any descriptive attributes of the relationship set. Example: schema for relationship set advisor ▪ advisor = (s_id, i_id)
  • 50.
    Redundancy of Schemas ▪Many-to-one and one-to-many relationship sets that are total on the many- side can be represented by adding an extra attribute to the “many” side, containing the primary key of the “one” side Example: Instead of creating a schema for relationship set inst_dept, add an attribute dept_name to the schema arising from entity set instructor Example ▪ ▪
  • 51.
    Redundancy of Schemas(Cont.) ▪ For one-to-one relationship sets, either side can be chosen to act as the “many” side • That is, an extra attribute can be added to either of the tables corresponding to the two entity sets If participation is partial on the “many” side, replacing a schema by an extra attribute in the schema corresponding to the “many” side could result in null values ▪
  • 52.
    Redundancy of Schemas(Cont.) ▪ The schema corresponding to a relationship set linking a weak entity set to its identifying strong entity set is redundant. Example: The section schema already contains the attributes that would appear in the sec_course schema ▪
  • 53.
  • 54.
    Specialization ▪ Top-down designprocess; 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 higher-level entity set. Depicted by a triangle component labeled ISA (e.g., instructor “is a” person). 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. ▪ ▪ ▪
  • 55.
    Specialization Example ▪ ▪ ▪ Overlapping –employee and student Disjoint – instructor and secretary Total and partial
  • 56.
    Representing Specialization viaSchemas ▪ Method 1: • Form a schema for the higher-level entity • Form a schema for each lower-level entity set, include primary key of higher-level entity set and local attributes • Drawback: getting information about, an employee requires accessing two relations, the one corresponding to the low-level schema and the one corresponding to the high-level schema
  • 57.
    Representing Specialization asSchemas (Cont.) ▪ Method 2: • Form a schema for each entity set with all local and inherited attributes • Drawback: name, street and city may be stored redundantly for people who are both students and employees
  • 58.
    Generalization ▪ A bottom-updesign 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. ▪ ▪
  • 59.
    Completeness constraint ▪ Completenessconstraint -- specifies whether or not an entity in the higher-level entity set must belong to at least one of the lower- level entity sets within a generalization. • 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
  • 60.
    Completeness constraint (Cont.) ▪ ▪ Partialgeneralization is the default. We can specify total generalization in an ER diagram by adding the keyword total in the diagram and drawing a dashed line from the keyword to the corresponding hollow arrow-head to which it applies (for a total generalization), or to the set of hollow arrow- heads to which it applies (for an overlapping generalization). The student generalization is total: All student entities must be either graduate or undergraduate. Because the higher-level entity set arrived at through generalization is generally composed of only those entities in the lower-level entity sets, the completeness constraint for a generalized higher-level entity set is usually total ▪
  • 61.
    Aggregation ▪ ▪ Consider the ternaryrelationship proj_guide, which we saw earlier Suppose we want to record evaluations of a student by a guide on a project
  • 62.
    Aggregation (Cont.) ▪ Relationshipsets eval_for and proj_guide represent overlapping information • Every eval_for relationship corresponds to a proj_guide relationship • However, some proj_guide relationships may not correspond to any eval_for relationships ▪So we can’t discard the proj_guide relationship Eliminate this redundancy via aggregation • Treat relationship as an abstract entity • Allows relationships between relationships • Abstraction of relationship into new entity ▪
  • 63.
    Aggregation (Cont.) ▪ Eliminatethis redundancy via aggregation without introducing redundancy, the following diagram represents: • A student is guided by a particular instructor on a particular project • A student, instructor, project combination may have an associated evaluation
  • 64.
    Reduction to RelationalSchemas ▪ To represent aggregation, create a schema containing • Primary key of the aggregated relationship, • The primary key of the associated entity set •Any descriptive attributes In our example: ▪ • The schema eval_for is: eval_for (s_ID, project_id, i_ID, evaluation_id) • The schema proj_guide is redundant.
  • 65.
  • 66.
    Common Mistakes inE-R Diagrams ▪ Example of erroneous E-R diagrams
  • 67.
    Common Mistakes inE-R Diagrams (Cont.)
  • 68.
    Entities vs. Attributes ▪Use of entity sets vs. attributes ▪ Use of phone as an entity allows extra information about phone numbers (plus multiple phone numbers)
  • 69.
    Entities vs. Relationshipsets ▪ Use of entity sets vs. relationship sets Possible guideline is to designate a relationship set to describe an action that occurs between entities ▪ Placement of relationship attributes For example, attribute date as attribute of advisor or as attribute of student
  • 70.
    Binary Vs. Non-BinaryRelationships ▪ Although it is possible to replace any non-binary (n-ary, for n > 2) relationship set by a number of distinct binary relationship sets, a n-ary relationship set shows more clearly that several entities participate in a single relationship. Some relationships that appear to be non-binary may be better represented using binary relationships • For example, a ternary relationship parents, relating a child to his/her father and mother, is best replaced by two binary relationships, father and mother ▪ ▪ Using two binary relationships allows partial information (e.g., only mother being known) • But there are some relationships that are naturally non- binary ▪ Example: proj_guide
  • 71.
    Converting Non-Binary Relationshipsto Binary Form ▪ In general, any non-binary relationship can be represented using binary relationships by creating an artificial entity set. • Replace R between entity sets A, B and C by an entity set E, and three relationship sets: 1. RA, relating E and A 2. RB, relating E and B 3. RC, relating E and C • Create an identifying attribute for E and add any attributes of R to E • For each relationship (ai , bi , ci) in R, create 1. a new entity ei in the entity set E 2. add (ei , ai ) to RA 3. add (ei , bi ) to RB 4. add (ei , ci ) to RC
  • 72.
    Converting Non-Binary Relationships(Cont.) ▪ Also need to translate constraints • Translating all constraints may not be possible • There may be instances in the translated schema that cannot correspond to any instance of R ▪ Exercise: add constraints to the relationships RA, RB and RC to ensure that a newly created entity corresponds to exactly one entity in each of entity sets A, B and C • We can avoid creating an identifying attribute by making E a weak entity set (described shortly) identified by the three relationship sets
  • 73.
    E-R Design Decisions ▪ ▪ Theuse of an attribute or entity set to represent an object. Whether a real-world concept is best expressed by an entity set or a relationship set. The use of a ternary relationship versus a pair of binary relationships. The use of a strong or weak entity set. The use of specialization/generalization – contributes to modularity in the design. The use of aggregation – can treat the aggregate entity set as a single unit without concern for the details of its internal structure. ▪ ▪ ▪ ▪
  • 74.
    Summary of SymbolsUsed in E-R Notation
  • 75.
    Symbols Used inE-R Notation (Cont.)
  • 76.
  • 77.
    Alternative ER Notations ChenIDE1FX (Crows feet notation)
  • 78.
    UML ▪ ▪ UML: Unified ModelingLanguage UML has many components to graphically model different aspects of an entire software system UML Class Diagrams correspond to E-R Diagram, but several differences. ▪
  • 79.
    ER vs. UMLClass Diagrams * Note reversal of position in cardinality constraint depiction
  • 80.
    ER vs. UMLClass Diagrams ER Diagram Notation Equivalent in UML * Generalization can use merged or separate arrows independent of disjoint/overlapping
  • 81.
    UML Class Diagrams(Cont.) ▪ Binary relationship sets are represented in UML by just drawing a line connecting the entity sets. The relationship set name is written adjacent to the line. The role played by an entity set in a relationship set may also be specified by writing the role name on the line, adjacent to the entity set. The relationship set name may alternatively be written in a box, along with attributes of the relationship set, and the box is connected, using a dotted line, to the line depicting the relationship set. ▪ ▪
  • 82.
    ER vs. UMLClass Diagrams
  • 83.
    Other Aspects ofDatabase Design Database System Concepts - 7th 6.83 ©Silberschatz, Korth and ▪ ▪ ▪ Functional Requirements Data Flow, Workflow Schema Evolution
  • 84.
    End of Chapter 6 DatabaseSystem Concepts - 7th 6.84 ©Silberschatz, Korth and