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You are here:Open notes-->VTU-->-Database-Management-System-10CS54-VTU-unit-2

Database Management System 10CS54 VTU unit-2

UNIT 2 ENTITY.1 Data Models, Schemas, and Instances



A data model ---a collection of concepts that can be used to describe the conceptual/logical 
structure of a database--- provides the necessary means to achieve this abstraction.
By structure is meant the data types, relationships, and constraints that should hold for the data. 
Most data models also include a set of basic operations for specifying retrievals/updates
Object-oriented data models include the idea of objects having behavior (i.e., applicable 
methods) being stored in the database (as opposed to purely "passive" data).


 
According to C.J. Date (one of the leading database experts) A data model is an abstract self contained logical defenition of the object and so forth that together constitute the abstract machine with which users interact. The objects allow us to model the structure of data; the operators allow us to model its behavior. 

In the relational data model, data is viewed as being organized in two-dimensional tables 
comprised of tuples of attribute values. This model has operations such as Project, Select, and 
Join

A data model is not to be confused with its implementation, which is a physical realization on a 
real machine of the components of the abstract machine that together constitute that model.
 
Categories of Data Models (based on degree of abstractness): 
 high-level/conceptual: (e.g., ER model of Chapter 3) provides a view close to the way 
users would perceive data; uses concepts such as 
o entity: real-world object or concept (e.g., student, employee, course, department, 
event) 
o attribute: some property of interest describing an entity (e.g., height, age, color) 
o relationship: an interaction among entities (e.g., works-on relationship between 
an employee and a project)
Representational/implementational: intermediate level of abstractness; example is 
relational data model (or the network model alluded to earlier). Also called record-based
model. 
 Low-level/physical: gives details as to how data is stored in computer system, such as 
record formats, orderings of records, access paths

2.1.2: Schemas, Instances, and Database State
One must distinguish between the description of a database and the database itself. The former is 
called the database schema, which is specified during design and is not expected to change 
often. (See Figure 2.1, p. 33, for schema diagram for relational UNIVERSITY database.) 
The actual data stored in the database probably changes often. The data in the database at a 
particular time is called the state of the database, or a snapshot. 
Application requirements change occasionally, which is one of the reasons why software 
maintenance is important. On such occasions, a change to a database's schema may be called for. 
An example would be to add a Date_of_Birth field/attribute to the STUDENT table. Making 
changes to a database schema is known as schema evolution. Most modern DBMS's support 
schema evolution operations that can be applied while a database is operational. 

2.2 DBMS Architecture and Data Independence
2.2.1: Three-Schema Architecture: This idea was first described by 
the ANSI/SPARC committee in late 1970's. The goal is to separate (i.e., insert layers of 
"insulation" between) user applications and the physical database. C.J. Date points out that it is 
an ideal that few, if any, real-life DBMS's achieve fully. 
 internal level: has an internal/physical schema that describes the physical storage 
structure of the database using a low-level data model)
 Conceptual level: has a conceptual schema describing the (logical) structure of the whole 
database for a community of users. It hides physical storage details, concentrating upon 
describing entities, data types, relationships, user operations, and constraints. Can be 
described using either high-level or implementational data model. 
 External/view level: includes a number of external schemas (or user views), each of 
which describes part of the database that a particular category of users is interested in, 
hiding rest of database. Can be described using either high-level or implementational data 
model. (In practice, usually described using same model as is the conceptual schema.) 
Users (including application programs) submit queries that are expressed with respect to the 
external level.
2.2 DBMS Architecture and Data Independence
2.2.1: Three-Schema Architecture: (See Figure 2.2, page 34.) This idea was first described by 
the ANSI/SPARC committee in late 1970's. The goal is to separate (i.e., insert layers of 
"insulation" between) user applications and the physical database. C.J. Date points out that it is 
an ideal that few, if any, real-life DBMS's achieve fully. 
 internal level: has an internal/physical schema that describes the physical storage 
structure of the database using a low-level data model)
 Conceptual level: has a conceptual schema describing the (logical) structure of the whole 
database for a community of users. It hides physical storage details, concentrating upon 
describing entities, data types, relationships, user operations, and constraints. Can be 
described using either high-level or implementational data model. 
 External/view level: includes a number of external schemas (or user views), each of 
which describes part of the database that a particular category of users is interested in, 
hiding rest of database. Can be described using either high-level or implementational data 
model. (In practice, usually described using same model as is the conceptual schema.) 
Users (including application programs) submit queries that are expressed with respect to the 
external level.
the functions needed by DDL, VDL, and DML languages. (Early versions of SQL had features in 
support of SDL functions, but no more.) 
2.3.1 DBMS Languages
Menu-based, forms-based, gui-based, natural language, special purpose for parametric users, for 
DBA. 
2.3.2 DBMS Interfaces
 Menu-based interfaces for web clients or browsing 
 Forms-based interfaces 
 GUI's 
 Natural Language Interfaces 
 Speech Input and Output 
 Interfaces for parametric users 
 Interfaces for the DBA 
2.4 Database System Environment
See Figure 2.3, page 41. 
2.5 Centralized and Client/Server Architectures for DBMS's
2.6 Classification of DBMS's
Based upon 
 underlying data model (e.g., relational, object, object-relational, network) 
 multi-user vs. single-user 
 centralized vs. distributed 
 cost 
 general-purpose vs. special-purpose 
 types of access path options 
2.7 Data Modeling Using the Entity-Relationship Model 
Outline of Database Design
The main phases of database design are depicted in Figure 3.1, page 59: 
 Requirements Collection and Analysis: purpose is to produce a description of the users' 
requirements.
 Conceptual Design: purpose is to produce a conceptual schema for the database, 
including detailed descriptions of entity types, relationship types, and constraints. All 
these are expressed in terms provided by the data model being used. (Remark: As the ER 
model is focused on precisely these three concepts, it would seem that the authors are 
predisposed to using that data model!) 
 Implementation: purpose is to transform the conceptual schema (which is at a 
high/abstract level) into a (lower-level) representational/implementational model 
supported by whatever DBMS is to be used. 
 Physical Design: purpose is to decide upon the internal storage structures, access paths 
(indexes), etc., that will be used in realizing the representational model produced in 
previous phase. 
2.8: Entity-Relationship (ER) Model

Our focus now is on the second phase, conceptual design, for which The Entity-Relationship 
(ER) Model is a popular high-level conceptual data model. 
In the ER model, the main concepts are entity, attribute, and relationship. 
2.8.1 Entities and Attributes
Entity: An entity represents some "thing" (in the miniworld) that is of interest to us, i.e., about 
which we want to maintain some data. An entity could represent a physical object (e.g., house, 
person, automobile, widget) or a less tangible concept (e.g., company, job, academic course). 
Attribute: An entity is described by its attributes, which are properties characterizing it. Each 
attribute has a value drawn from some domain (set of meaningful values). 
Example: A PERSON entity might be described by Name, BirthDate, Sex, etc., attributes, each 
having a particular value. 
What distinguishes an entity from an attribute is that the latter is strictly for the purpose of 
describing the former and is not, in and of itself, of interest to us. It is sometimes said that an 
entity has an independent existence, whereas an attribute does not. In performing data modeling, 
however, it is not always clear whether a particular concept deserves to be classified as an entity 
or "only" as an attribute. 
We can classify attributes along these dimensions: 
 simple/atomic vs. composite 
 single-valued vs. multi-valued (or set-valued) 
 Stored vs. derived (Note from instructor: this seems like an implementational detail that 
ought not to be considered at this (high) level of abstraction.) 
A composite attribute is one that is composed of smaller parts. An atomic attribute is indivisible 
or indecomposable. 
 Example 1: A BirthDate attribute can be viewed as being composed of (sub-)attributes 
for month, day, and year. 
 Example 2: An Address attribute (Figure 3.4, page 64) can be viewed as being composed 
of (sub-)attributes for street address, city, state, and zip code. A street address can itself 
be viewed as being composed of a number, street name, and apartment number. As this 
suggests, composition can extend to a depth of two (as here) or more. 
To describe the structure of a composite attribute, one can draw a tree (as in the aforementioned 
Figure 3.4). In case we are limited to using text, it is customary to write its name followed by a 
parenthesized list of its sub-attributes. For the examples mentioned above, we would write 
BirthDate(Month,Day,Year)
Address(StreetAddr(StrNum, StrName, AptNum), City, State, Zip)
Single- vs. multi-valued attribute: Consider a PERSON entity. The person it represents has 
(one) SSN, (one) date of birth, (one, although composite) name, etc. But that person may have 
zero or more academic degrees, dependents, or (if the person is a male living in Utah) spouses! 
How can we model this via attributes AcademicDegrees, Dependents, and Spouses? One way is 
to allow such attributes to be multi-valued (perhaps set-valued is a better term), which is to say 
that we assign to them a (possibly empty) set of values rather than a single value. 
To distinguish a multi-valued attribute from a single-valued one, it is customary to enclose the 
former within curly braces (which makes sense, as such an attribute has a value that is a set, and 
curly braces are traditionally used to denote sets). Using the PERSON example from above, we 
would depict its structure in text as 
PERSON(SSN, Name, BirthDate(Month, Day, Year), { AcademicDegrees(School, Level, Year) 
}, { Dependents }, ...)
Here we have taken the liberty to assume that each academic degree is described by a school, 
level (e.g., B.S., Ph.D.), and year. Thus, AcademicDegrees is not only multi-valued but also 
composite. We refer to an attribute that involves some combination of multi-valuedness and
compositeness as a complex attribute. 
A more complicated example of a complex attribute is AddressPhone in Figure 3.5 (page 65). 
This attribute is for recording data regarding addresses and phone numbers of a business. The 
structure of this attribute allows for the business to have several offices, each described by an 
address and a set of phone numbers that ring into that office. Its structure is given by 
{ AddressPhone( { Phone(AreaCode, Number) }, Address(StrAddr(StrNum, StrName, 
AptNum), City, State, Zip)) } 
Stored vs. derived attribute: Perhaps independent and derivable would be better terms for these 
(or non-redundant and redundant). In any case, a derived attribute is one whose value can be 
calculated from the values of other attributes, and hence need not be stored. Example: Age can 
be calculated from BirthDate, assuming that the current date is accessible. 
The Null value: In some cases a particular entity might not have an applicable value for a 
particular attribute. Or that value may be unknown. Or, in the case of a multi-valued attribute, the 
appropriate value might be the empty set. 
Example: The attribute DateOfDeath is not applicable to a living person and its correct value 
may be unknown for some persons who have died. 
In such cases, we use a special attribute value (non-value?), called null. There has been some 
argument in the database literature about whether a different approach (such as having distinct 
values for not applicable and unknown) would be superior. 
2.8.2: Entity Types, Entity Sets, Keys, and Domains
Above we mentioned the concept of a PERSON entity, i.e., a representation of a particular 
person via the use of attributes such as Name, Sex, etc. Chances are good that, in a database in 
which one such entity exists, we will want many others of the same kind to exist also, each of 
them described by the same collection of attributes. Of course, the values of those attributes will 
differ from one entity to another (e.g., one person will have the name "Mary" and another will 
have the name "Rumpelstiltskin"). Just as likely is that we will want our database to store 
information about other kinds of entities, such as business transactions or academic courses, 
which will be described by entirely different collections of attributes. 
This illustrates the distinction between entity types and entity instances. An entity type serves as 
a template for a collection of entity instances, all of which are described by the same collection 
of attributes. That is, an entity type is analogous to a class in object-oriented programming and 
an entity instance is analogous to a particular object (i.e., instance of a class). 
In ER modeling, we deal only with entity types, not with instances. In an ER diagram, each 
entity type is denoted by a rectangular box. 
An entity set is the collection of all entities of a particular type that exist, in a database, at some 
moment in time. 
Key Attributes of an Entity Type: A minimal collection of attributes (often only one) that, by 
design, distinguishes any two (simultaneously-existing) entities of that type. In other words, if 
attributes A1 through Am together form a key of entity type E, and e and f are two entities of type 
E existing at the same time, then, in at least one of the attributes Ai (0 < i <= m), e and f must 
have distinct values. 
An entity type could have more than one key. (An example of this appears in Figure 3.7, page 
67, in which the CAR entity type is postulated to have both { Registration(RegistrationNum, 
State) } and { VehicleID } as keys.) 
Domains (Value Sets) of Attributes: The domain of an attribute is the "universe of values" from 
which its value can be drawn. In other words, an attribute's domain specifies its set of allowable 
values. The concept is similar to data type. 
Example Database Application: COMPANY
Suppose that Requirements Collection and Analysis results in the following (informal) 
description of the COMPANY miniworld: 
The company is organized as a collection of departments. 
 Each department 
o has a unique name 
o has a unique number 
o is associated with a set of locations 
o has a particular employee who acts as its manager (and who assumed that position 
on some date) 
o has a set of employees assigned to it 
o controls a set of projects 
 Each project 
o has a unique name 
o has a unique number 
o has a single location 
o has a set of employees who work on it 
o is controlled by a single department 
 Each employee 
o has a name 
o has a SSN that uniquely identifies her/him 
o has an address 
o has a salary 
o has a sex 
o has a birthdate 
o has a direct supervisor 
o has a set of dependents 
o is assigned to one department 
o works some number of hours per week on each of a set of projects (which need 
not all be controlled by the same department) 
 Each dependent 
o has first name 
o has a sex 
o has a birthdate 
o is related to a particular employee in a particular way (e.g., child, spouse, pet) 
o is uniquely identified by the combination of her/his first name and the employee 
of which (s)he is a dependent 
2.8.3 Initial Conceptual Design of COMPANY database
Using the above structured description as a guide, we get the following preliminary design for 
entity types and their attributes in the COMPANY database: 
 DEPARTMENT(Name, Number, { Locations }, Manager, ManagerStartDate, { 
Employees }, { Projects }) 
 PROJECT(Name, Number, Location, { Workers }, ControllingDept) 
 EMPLOYEE(Name(FName, MInit, LName), SSN, Sex, Address, Salary, BirthDate, 
Dept, Supervisor, { Dependents }, { WorksOn(Project, Hours) }) 
 DEPENDENT(Employee, FirstName, Sex, BirthDate, Relationship) 
Remarks: Note that the attribute WorksOn of EMPLOYEE (which records on which projects the 
employee works) is not only multi-valued (because there may be several such projects) but also 
composite, because we want to record, for each such project, the number of hours per week that 
the employee works on it. Also, each candidate key has been indicated by underlining. 
For similar reasons, the attributes Manager and ManagerStartDate of DEPARTMENT really 
ought to be combined into a single composite attribute. Not doing so causes little or no harm, 
however, because these are single-valued attributes. Multi-valued attributes would pose some 
difficulties, on the other hand. Suppose, for example, that a department could have two or more 
managers, and that some department had managers Mary and Harry, whose start dates were 10-
4-1999 and 1-13-2001, respectively. Then the values of the Manager and ManagerStartDate
attributes should be { Mary, Harry } and { 10-4-1999, 1-13-2001 }. But from these two attribute 
values, there is no way to determine which manager started on which date. On the other hand, by 
recording this data as a set of ordered pairs, in which each pair identifies a manager and her/his 
starting date, this deficiency is eliminated. End of Remarks
2.9 Relationship Types, Sets, Roles, and Structural Constraints
Having presented a preliminary database schema for COMPANY, it is now convenient to clarify 
the concept of a relationship (which is the last of the three main concepts involved in the ER 
model). 
Relationship: This is an association between two entities. As an example, one can imagine a 
STUDENT entity being associated to an ACADEMIC_COURSE entity via, say, an 
ENROLLED_IN relationship. 
Whenever an attribute of one entity type refers to an entity (of the same or different entity type), 
we say that a relationship exists between the two entity types. 
From our preliminary COMPANY schema, we identify the following relationship types (using 
descriptive names and ordering the participating entity types so that the resulting phrase will be 
in active voice rather than passive): 
 EMPLOYEE MANAGES DEPARTMENT (arising from Manager attribute in 
DEPARTMENT) 
 DEPARTMENT CONTROLS PROJECT (arising from ControllingDept attribute in 
PROJECT and the Projects attribute in DEPARTMENT) 
 EMPLOYEE WORKS_FOR DEPARTMENT (arising from Dept attribute in 
EMPLOYEE and the Employees attribute in DEPARTMENT) 
 EMPLOYEE SUPERVISES EMPLOYEE (arising from Supervisor attribute in 
EMPLOYEE) 
 EMPLOYEE WORKS_ON PROJECT (arising from WorksOn attribute in EMPLOYEE 
and the Workers attribute in PROJECT) 
 DEPENDENT DEPENDS_ON EMPLOYEE (arising from Employee attribute in 
DEPENDENT and the Dependents attribute in EMPLOYEE) 
In ER diagrams, relationship types are drawn as diamond-shaped boxes connected by lines to the 
entity types involved. See Figure 3.2, page 62. Note that attributes are depicted by ovals 
connected by lines to the entity types they describe (with multi-valued attributes in double ovals 
and composite attributes depicted by trees). The original attributes that gave rise to the 
relationship types are absent, having been replaced by the relationship types. 
A relationship set is a set of instances of a relationship type. If, say, R is a relationship type that 
relates entity types A and B, then, at any moment in time, the relationship set of R will be a set of 
ordered pairs (x,y), where x is an instance of A and y is an instance of B. What this means is that, 
for example, if our COMPANY miniworld is, at some moment, such that employees e1, e3, and 
e6 work for department d1, employees e2 and e4 work for department d2, and employees e5 and e7
work for department d3, then the WORKS_FOR relationship set will include as instances the 
ordered pairs (e1, d1), (e2, d2), (e3, d1), (e4, d2), (e5, d3), (e6, d1), and (e7, d3). See Figure 3.9 on 
page 71 for a graphical depiction of this. 
2.9.1 Ordering of entity types in relationship types: Note that the order in which we list the 
entity types in describing a relationship is of little consequence, except that the relationship name 
(for purposes of clarity) ought to be consistent with it. For example, if we swap the two entity 
types in each of the first two relationships listed above, we should rename them 
IS_MANAGED_BY and IS_CONTROLLED_BY, respectively. 
2.9.2 Degree of a relationship type: Also note that, in our COMPANY example, all relationship 
instances will be ordered pairs, as each relationship associates an instance from one entity type 
with an instance of another (or the same, in the case of SUPERVISES) relationship type. Such 
relationships are said to be binary, or to have degree two. Relationships with degree three (called 
ternary) or more are also possible, although not as common. This is illustrated in Figure 3.10 
(page 72), where a relationship SUPPLY (perhaps not the best choice for a name) has as 
instances ordered triples of suppliers, parts, and projects, with the intent being that inclusion of 
the ordered triple (s2, p4, j1), for example, indicates that supplier s2 supplied part p4 to project j1). 
Roles in relationships: Each entity that participates in a relationship plays a particular role in 
that relationship, and it is often convenient to refer to that role using an appropriate name. For 
example, in each instance of a WORKS_FOR relationship set, the employee entity plays the role 
of worker or (surprise!) employee and each department plays the role of employer or (surprise!) 
department. Indeed, as this example suggests, often it is best to use the same name for the role as 
for the corresponding entity type. 
An exception to this rule occurs when the same entity type plays two (or more) roles in the same 
relationship. (Such relationships are said to be reCURsive, which I find to be a misleading use of 
that term. A better term might be self-referential.) For example, in each instance of a 
SUPERVISES relationship set, one employee plays the role of supervisor and the other plays the 
role of supervisee. 
2.9.3 Constraints on Relationship Types
Often, in order to make a relationship type be an accurate model of the miniworld concepts that it 
is intended to represent, we impose certain constraints that limit the possible corresponding 
relationship sets. (That is, a constraint may make "invalid" a particular set of instances for a 
relationship type.) 
There are two main kinds of relationship constraints (on binary relationships). For illustration, let 
R be a relationship set consisting of ordered pairs of instances of entity types A and B, 
respectively. 
 cardinality ratio: 
o 1:1 (one-to-one): Under this constraint, no instance of A may particpate in more 
than one instance of R; similarly for instances of B. In other words, if (a1, b1) and 
(a2, b2) are (distinct) instances of R, then neither a1 = a2 nor b1 = b2. 
Example: Our informal description of COMPANY says that every department 
has one employee who manages it. If we also stipulate that an employee may not 
(simultaneously) play the role of manager for more than one department, it 
follows that MANAGES is 1:1. 
o 1:N (one-to-many): Under this constraint, no instance of B may participate in 
more than one instance of R, but instances of A are under no such restriction. In 
other words, if (a1, b1) and (a2, b2) are (distinct) instances of R, then it cannot be 
the case that b1 = b2. 
Example: CONTROLS is 1:N because no project may be controlled by more than 
one department. On the other hand, a department may control any number of 
projects, so there is no restriction on the number of relationship instances in which 
a particular department instance may participate. For similar reasons, 
SUPERVISES is also 1:N. 
o N:1 (many-to-one): This is just the same as 1:N but with roles of the two entity 
types reversed. 
Example: WORKS_FOR and DEPENDS_ON are N:1. 
o M:N (many-to-many): Under this constraint, there are no restrictions. (Hence, 
the term applies to the absence of a constraint!) 
Example: WORKS_ON is M:N, because an employee may work on any number 
of projects and a project may have any number of employees who work on it. 
Notice the notation in Figure 3.2 for indicating each relationship type's cardinality ratio. 
Suppose that, in designing a database, we decide to include a binary relationship R as 
described above (which relates entity types A and B, respectively). To determine how R
should be constrained, with respect to cardinality ratio, the questions you should ask are 
these
May a given entity of type B be related to multiple entities of type A? 
May a given entity of type A be related to multiple entities of type B? 
The pair of answers you get maps into the four possible cardinality ratios as follows: 
(yes,yes)-->M:N
(yes,no)-->N:1
(no,yes)-->1:N
(no, no) --> 1:1
 participation: specifies whether or not the existence of an entity depends upon its being 
related to another entity via the relationship. 
o total participation (or existence dependency): To say that entity type A is 
constrained to participate totally in relationship R is to say that if (at some 
moment in time) R's instance set is { (a1, b1), (a2, b2), ... (am, bm) }, 
then (at that same moment) A's instance set must be { a1, a2, ..., am }. In other 
words, there can be no member of A's instance set that does not participate in at 
least one instance of R. 
According to our informal description of COMPANY, every employee must be 
assigned to some department. That is, every employee instance must participate in 
at least one instance of WORKS_FOR, which is to say that EMPLOYEE satisfies 
the total participation constraint with respect to the WORKS_FOR relationship. 
In an ER diagram, if entity type A must participate totally in relationship type R, 
the two are connected by a double line. See Figure 3.2. 
o partial participation: the absence of the total participation constraint! (E.g., not 
every employee has to participate in MANAGES; hence we say that, with respect 
to MANAGES, EMPLOYEE participates partially. This is not to say that for all 
employees to be managers is not allowed; it only says that it need not be the case 
that all employees are managers. 
2.9.4 Attributes of Relationship Types
Relationship types, like entity types, can have attributes. A good example is WORKS_ON, each 
instance of which identifies an employee and a project on which (s)he works. In order to record 
(as the specifications indicate) how many hours are worked by each employee on each project, 
we include Hours as an attribute of WORKS_ON. (See Figure 3.2 again.) In the case of an M:N 
relationship type (such as WORKS_ON), allowing attributes is vital. In the case of an N:1, 1:N, 
or 1:1 relationship type, any attributes can be assigned to the entity type opposite from the 1 side. 
For example, the StartDate attribute of the MANAGES relationship type can be given to either 
the EMPLOYEE or the DEPARTMENT entity type. 
2.10 Weak Entity Types: An entity type that has no set of attributes that qualify as a key is 
called weak. (Ones that do are strong.) 
An entity of a weak identity type is uniquely identified by the specific entity to which it is related 
(by a so-called identifying relationship that relates the weak entity type with its so-called 
identifying or owner entity type) in combination with some set of its own attributes (called a 
partial key). 
Example: A DEPENDENT entity is identified by its first name together with the EMPLOYEE
entity to which it is related via DEPENDS_ON. (Note that this wouldn't work for former 
heavyweight boxing champion George Foreman's sons, as they all have the name "George"!) 
Because an entity of a weak entity type cannot be identified otherwise, that type has a total 
participation constraint (i.e., existence dependency) with respect to the identifying 
relationship. 
This should not be taken to mean that any entity type on which a total participation constraint 
exists is weak. For example, DEPARTMENT has a total participation constraint with respect to 
MANAGES, but it is not weak. 
In an ER diagram, a weak entity type is depicted with a double rectangle and an identifying
relationship type is depicted with a double diamond. 
Design Choices for ER Conceptual Design: Sometimes it is not clear whether a particular 
miniworld concept ought to be modeled as an entity type, an attribute, or a relationship type. 
Here are some guidelines (given with the understanding that schema design is an iterative 
process in which an initial design is refined repeatedly until a satisfactory result is achieved): 
 As happened in our development of the ER model for COMPANY, if an attribute of 
entity type A serves as a reference to an entity of type B, it may be wise to refine that 
attribute into a binary relationship involving entity types A and B. It may well be that B
has a corresponding attribute referring back to A, in which case it, too, is refined into the 
aforementioned relationship. In our COMPANY example, this was exemplified by the 
Projects and ControllingDept attributes of DEPARTMENT and PROJECT, respectively. 
 An attribute that exists in several entity types may be refined into its own entity type. For 
example, suppose that in a UNIVERSITY database we have entity types STUDENT, 
INSTRUCTOR, and COURSE, all of which have a Department attribute. Then it may be 
wise to introduce a new entity type, DEPARTMENT, and then to follow the preceding 
guideline by introducing a binary relationship between DEPARTMENT and each of the 
three aforementioned entity types. 
 An entity type that is involved in very few relationships (say, zero, one, or possibly two) 
could be refined into an attribute (of each entity type to which it is related). 

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