数据库设计与新技术应用：逻辑设计指南（第五版）

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a. Conceptual data modeling. The data requirements are

analyzed and modeled by using an ER or UML dia-

gram that includes many features we will study in

Chapters 2 and 3, for example, semantics for optional

relationships, ternary relationships, supertypes, and

subtypes (categories). Processing requirements are

typically specified using natural language expressions

or SQL commands along with the frequency of occur-

rence. Figure 1.2 (S

tep II.a) shows a

possible ER

Information Requirements

Determine requirements

Logical Design

[multiple views]

Model Integrate views

[single view]

Transform to SQL tables

Normalize

Physical Design

Select indexes

[special requirements]

Denormalize

[else]

Implementation

Implement

[else]

Monitor and detect changing requirements

[defunct]

Figure 1.1 The database life

cycle.

4 Chapter 1 INTRODUCTION

c. Transformation of the conceptual data model to SQL

tables. Based on a categorization of data modeling con-

structs and a set of mapping rules, each relationship

and its associated entities are transformed into a set of

DBMS-specific candidate relational tables. We will

show these transformations in standard SQL in Chapter

5. Redundant tables are eliminated as part of this pro-

cess. In our example, the tables in Step II.c of Figure 1.2

are the r

esult of transformation of the integrated ER

model in Step II.b .

d. Norm

aliza tion of tables. Given a

table (R), a set of

attributes (B) is functio nally dependent on another

set of attributes (A) if, at each instant of time, each

A value is associated with exactly one B value. Func-

tional dependencies (FDs) are derived from the con-

ceptual d ata m ode l d iagra m a nd the semantics of

data relationships in the requirements analy sis. They

represent the dependencies among data elements

that are unique i dent ifiers (keys ) of e ntiti es. Addi-

tional FDs, which repre sen t the dependencies

between key and nonkey attributes within entities,

canbederivedfromtherequirementsspecification.

Candidate relational tables associated with all

derived FDs are normalized (i.e., modified by

decomposing or spli tting tables into smaller tables)

using standard normalization techniques. Finally,

redundancies in the data that occur in normalized

candidate tables are analyzed further for possibl e

elimination, with the constraint that data integrity

must be preserved. An example of normalization of

the Salesperson table into the new Salesperson and

SalesVacations tables is shown in

Figure 1.2 from

tep II.c t

o Step II.d.

We note here that database tool vendors tend to use

the term logical model to refer to

the conceptual data

model, and they use the term physical model to refer

to the DBMS-specific implementation model (e.g.,

SQL tables). We also note that many conceptual data

models are obtained not from scratch, but from the

process of reverse engineering from an existing

DBMS-specific schema (Silberschatz et al., 2010).

Chapter 1 INTRODUCTION 7

III. Physical design. The physical design step involves the

selection of indexes (access methods), partitioning,

and clustering of data. The logical design methodology

in Step II simplifies the approach to designing large rela-

tional databases by reducing the number of data

dependencies that need to be analyzed. This is accom-

plished by inserting the conceptual data modeling and

integration steps (Steps II.a and II.b of Figure 1.2)

into

the t

raditional relational design approach. The objective

of these steps is an accurate representation of reality.

Data integrity is preserved through normalization of the

candidate tables created when the conceptual data

model is transformed into a relational model. The pur-

pose of physical design is to then optimize performance.

As part of the physical design, the global schema can

sometimes

be refined in limited

ways to reflect pro-

cessing (query and transaction) requirements if there

are obvious large gains to be made in efficiency. This

is called denormalization. It consists of selecting domi-

nant processes on the basis of high frequency, high vol-

ume, or explicit priority; defining simple extensions to

tables that will improve query performance; evaluating

total cost for query, update, and storage; and consider-

ing the side effects, such as possible loss of integrity.

This is particularly important for online analytical pro-

cessing (OLAP) applications.

IV.Database implementation, monitoring, and modifica-

tion. Once the design is completed, the database can be

created through implementation of the formal schema

using the data definition language (DDL) of a DBMS. Then

the data manipulation language (DML) can be used to

query and update the database, as well as to set up indexes

and establish constraints, such as referential integrity.

The language SQL contains both DDL and DML con-

structs; for example, the create table command represents

DDL, and the select command represents DML.

As the database begins operation, monitoring

indicates whether performance requirements are being

met. If they are not being satisfied, modifications should

be made to improve performance. Other modifications

may be necessary when requirements change or end

8 Chapter 1 INTRODUCTION

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