DATABASE MANAGEMENT SYSTEMS
B. Tech II/IT
in g UNIT-II PPT SLIDES n E O by Raghu Ramakrishnan o Text Books: (1) DBMS aD DBMS by Sudarshan and Korth a (2) F
.c II Semester rs ee
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INDEX UNIT-2 PPT SLIDES
S.NO
Module as per Lecture PPT Session planner No Slide NO -----------------------------------------------------------------------------------------1. History of Database Systems L1 L1- 1 to L1- 10 2. DB design and ER diagrams L2 L2- 1 to L2- 10 3. Relationships & sets L3 L3- 1 to L3- 5 4. Addn features of the ER model L4 L4- 1 to L4- 7 5. Addn features of the ER model L5 L5- 1 to L5- 6 6. Conceptual design with ER model L6 L6- 1 to L6 -6 7. Large enterprises L7 L7- 1 to L7- 3
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History of Database Systems
1950s and early 1960s: Data processing using magnetic tapes for storage Tapes provide only sequential access Punched cards for input Late 1960s and 1970s: Hard disks allow direct access to data Network and hierarchical data models in widespread use Ted Codd defines the relational data model Would win the ACM Turing Award for this work IBM Research begins System R prototype UC Berkeley begins Ingres prototype High-performance (for the era) transaction processing
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Slide No:L1-1
Magnetic tape unit
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Magnetic tape
Hard disk
Slide No:L1-2
History (cont.)
1980s: Research relational prototypes evolve into commercial systems SQL becomes industry standard Parallel and distributed database systems Object-oriented database systems 1990s: Large decision support and data-mining applications Large multi-terabyte data warehouses Emergence of Web commerce 2000s: XML and XQuery standards Automated database administration Increasing use of highly parallel database systems Web-scale distributed data storage systems
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Slide No:L1-3
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Slide No:L1-4
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Slide No:L1-5
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Slide No:L1-6
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Slide No:L1-7
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Slide No:L1-8
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Slide No:L1-9
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Slide No:L1-10
Database Design
Conceptual design: (ER Model is used at this stage.) What are the entities and relationships in the enterprise? What information about these entities and relationships should we store in the database? What are the integrity constraints or business rules that hold? A database `schema in the ER Model can be represented pictorially (ER diagrams). Can map an ER diagram into a relational schema.
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Slide No:L2-1
Modeling
A database can be modeled as: a collection of entities, relationship among entities. An entity is an object that exists and is distinguishable from other objects. Example: specific person, company, event, plant Entities have attributes Example: people have names and addresses 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
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Slide No:L2-2
Entity Sets customer and loan
customer_id customer_ customer_ customer_ name street city loan_ number amount
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Slide No:L2-3
Attributes
An entity is represented by a set of attributes, that is descriptive properties possessed by all members of an entity set.
Example: customer = (customer_id, customer_name, customer_street, customer_city ) loan = (loan_number, amount )
Domain the set of permitted values for each attribute 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
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Slide No:L2-4
Composite Attributes
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Slide No:L2-5
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
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Slide No:L2-6
Mapping Cardinalities
Note: Some elements in A and B may not be mapped to any elements in the other set
Slide No:L2-7
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One to one
One to many
Mapping Cardinalities
Note: Some elements in A and B may not be mapped to any elements in the other set
Slide No:L2-8
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Many to one
Many to many
ER Model Basics
name
ssn
Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. Entity Set: A collection of similar entities. E.g., all employees. All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!) Each entity set has a key. Each attribute has a domain.
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Employees
m o
lot
Slide No:L2-9
ER Model Basics (Contd.)
ssn since name ssn lot Employees Works_In did dname budget Departments
name
lot
Relationship: Association among two or more entities. E.g., Attishoo works in Pharmacy department. Relationship Set: Collection of similar relationships. An n-ary relationship set R relates n entity sets E1 ... En; each relationship in R involves entities e1 E1, ..., en En Same entity set could participate in different relationship sets, or in different roles in same set.
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supervisor
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Employees
subord inate
Reports_To
Slide No:L2-10
Relationship Sets
A relationship is an association among several entities Example: Hayes depositor A-102 customer entityrelationship setaccount 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: (Hayes, A-102) depositor
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Slide No:L3-1
Relationship Set borrower
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Slide No:L3-2
Relationship Sets (Cont.) An attribute can also be property of a relationship set. For instance, the depositor relationship set between entity sets customer and account may have the attribute access-date
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Slide No:L3-3
Degree of a Relationship Set
Refers to number of entity sets that participate in a relationship set. Relationship sets that involve two entity sets are binary (or degree two). Generally, most relationship sets in a database system are binary. Relationship sets may involve more than two entity sets.
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Slide No:L3-4
Degree of a Relationship Set
Example: Suppose employees of a bank may have jobs (responsibilities) at multiple branches, with different jobs at different branches. Then there is a ternary relationship set between entity sets employee, job, and branch
Relationships between more than two entity sets are rare. Most relationships are binary. (More on this later.)
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Slide No:L3-5
Additional features of the ER model Key Constraints
since name ssn dname
lot
did
budget
Employees
Manages
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.
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1-to-1
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Departments
1-to Many
Many-to-1 Many-to-Many
Slide No:L4-1
Participation Constraints
Does every department have a manager? If so, this is a participation constraint: the participation of Departments in Manages is said to be total (vs. partial). Every Departments entity must appear in an instance of the Manages relationship.
name ssn
Employees
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dname budget Departments
did
Manages
Works_In
since
Slide No:L4-2
Weak Entities
A weak entity can be identified uniquely only by considering the primary key of another (owner) entity. Owner entity set and weak entity set must participate in a one-to-many relationship set (one owner, many weak entities). Weak entity set must have total participation in this identifying relationship set.
name ssn
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cost Policy
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pname
age
Employees
Dependents
Slide No:L4-3
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 sets discriminator.
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Slide No:L4-4
Weak Entity Sets (Cont.)
We depict a weak entity set by double rectangles. We underline the discriminator of a weak entity set with a dashed line. payment_number discriminator of the payment entity set Primary key for payment (loan_number, payment_number)
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Slide No:L4-5
Weak Entity Sets (Cont.)
Note: the primary key of the strong entity set is not explicitly stored with the weak entity set, since it is implicit in the identifying relationship. If loan_number were explicitly stored, payment could be made a strong entity, but then the relationship between payment and loan would be duplicated by an implicit relationship defined by the attribute loan_number common to payment and loan
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Slide No:L4-6
More Weak Entity Set Examples
In a university, a course is a strong entity and a course_offering can be modeled as a weak entity The discriminator of course_offering would be semester (including year) and section_number (if there is more than one section) If we model course_offering as a strong entity we would model course_number as an attribute. Then the relationship with course would be implicit in the course_number attribute
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Slide No:L4-7
ISA (`is a) Hierarchies
name
As in C++, or other PLs,
ssn
lot
attributes are inherited. If we declare A ISA B, every A entity is also considered to be a B entity.
Employees hourly_wages hours_worked
Overlap constraints: Can Joe be an Hourly_Emps as well as a Contract_Emps entity? (Allowed/disallowed) Covering constraints: Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no) Reasons for using ISA: To add descriptive attributes specific to a subclass. To identify entitities that participate in a relationship.
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contractid Contract_Emps
ISA
Hourly_Emps
Slide No:L5-1
Aggregation
Used when we have to model a relationship involving (entitity sets and) a relationship set. Aggregation allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships.
name ssn Employees lot
a avs. ternary relationship: Aggregation F
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pid
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started_on
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Monitors
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since
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until dname budget
pbudget
did Departments
Projects
Sponsors
Monitors is a distinct relationship, with a descriptive attribute. Also, can say that each sponsorship is monitored by at most one employee.
Slide No:L5-2
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
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Slide No:L5-3
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 cant 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
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Slide No:L5-4
Aggregation (Cont.)
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
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Slide No:L5-5
E-R Diagram With Aggregation
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Slide No:L5-6
Conceptual Design Using the ER Model
Design choices: Should a concept be modeled as an entity or an attribute? Should a concept be modeled as an entity or a relationship? Identifying relationships: Binary or ternary? Aggregation? Constraints in the ER Model: A lot of data semantics can (and should) be captured. But some constraints cannot be captured in ER diagrams.
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Slide No:L6-1
Entity vs. Attribute
Should address be an attribute of Employees or an entity (connected to Employees by a relationship)? Depends upon the use we want to make of address information, and the semantics of the data: If we have several addresses per employee, address must be an entity (since attributes cannot be setvalued). If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modeled as an entity (since attribute values are atomic).
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Slide No:L6-2
Entity vs. Attribute (Contd.)
Works_In4 does not allow an employee to work in a department for two or more periods. Similar to the problem of wanting to record several addresses for an employee: We want to record several values of the descriptive attributes for each instance of this relationship. Accomplished by introducing new entity set, Duration.
from
lot to
name ssn
Employees
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Works_In4
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did
dname
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Departments
name did Works_In4
ssn Employees
dname
budget Departments
from
Slide No:L6-3
Duration
to
Entity vs. Relationship
First ER diagram OK if a manager gets a separate name discretionary budget for ssn each dept. What if a manager gets a Employees discretionary budget that covers all managed name depts? ssn Redundancy: dbudget stored for each dept Employees managed by manager. Misleading: Suggests ISA dbudget associated with department-mgr combination.
since lot
dbudget
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Manages2
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did
dname
budget
Departments
since did Manages2
dname budget Departments
Managers
Slide No:L6-4
dbudget
This fixes the problem!
Binary vs. Ternary Relationships
If each policy is owned by just 1 employee, and each dependent is tied to the covering policy, first diagram is inaccurate. What are the additional constraints in the 2nd diagram?
name ssn lot Employees Covers
Bad design
ssn
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name
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policyid
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Policies
m o
pname
age
Dependents
cost pname age
Employees Purchaser
Dependents
Beneficiary
Better design
Slide No:L6-5
Policies cost
policyid
Binary vs. Ternary Relationships (Contd.)
Previous example illustrated a case when two binary relationships were better than one ternary relationship. An example in the other direction: a ternary relation Contracts relates entity sets Parts, Departments and Suppliers, and has descriptive attribute qty. No combination of binary relationships is an adequate substitute: S can-supply P, D needs P, and D deals-with S does not imply that D has agreed to buy P from S. How do we record qty?
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Slide No:L6-6
Summary of Conceptual Design
Conceptual design follows requirements analysis, Yields a high-level description of data to be stored ER model popular for conceptual design Constructs are expressive, close to the way people think about their applications. Basic constructs: entities, relationships, and attributes (of entities and relationships). Some additional constructs: weak entities, ISA hierarchies, and aggregation. Note: There are many variations on ER model.
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Slide No:L7-1
Summary of ER (Contd.)
Several kinds of integrity constraints can be expressed in the ER model: key constraints, participation constraints, and overlap/covering constraints for ISA hierarchies. Some foreign key constraints are also implicit in the definition of a relationship set. Some constraints (notably, functional dependencies) cannot be expressed in the ER model. Constraints play an important role in determining the best database design for an enterprise.
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Slide No:L7-2
Summary of ER (Contd.)
ER design is subjective. There are often many ways to model a given scenario! Analyzing alternatives can be tricky, especially for a large enterprise. Common choices include: Entity vs. attribute, entity vs. relationship, binary or nary relationship, whether or not to use ISA hierarchies, and whether or not to use aggregation. Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful.
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Slide No:L7-3
