Examples of types of records are order, salesperson, customer, product, and department. A file is a collection of records of a single type. Database systems have built upon and expanded these definitions: In a relational database, a data item is called a column or attribute , a record is called a row or tuple , and a file is called a table.
A database is a more complex object; it is a collection of interrelated stored data that serves the needs of multiple users within one or more organizations—that is, an interrelated collection of many different types of tables.
The motivation for using databases rather than files has been greater availability to a diverse set of users, integration of data for easier access and update for complex transactions, and less redundancy of data. A database management system DBMS is a generalized software system for manipulating databases. A DBMS supports a logical view schema, subschema ; physical view access methods, data clustering ; data definition language; data manipulation language; and important utilities such as transaction management and concurrency control, data integrity, crash recovery, and security.
Relational database systems, the dominant type of systems for well-formatted business databases, also provide a greater degree of data independence than the earlier hierarchical and network CODASYL database management systems.
Data independence is the ability to make changes in either the logical or physical structure of the database without requiring reprogramming of application programs. It also makes database conversion and reorganization much easier. Relational DBMSs provide a much higher degree of data independence than previous systems; they are the focus of our discussion on data modeling.
The database life cycle incorporates the basic steps involved in designing a global schema of the logical database, allocating data across a computer network, and defining local DBMS-specific schemas. Once the design is completed, the life cycle continues with database implementation and maintenance. This chapter contains an overview of the database life cycle, as shown in Figure 1. In succeeding chapters we will focus on the database design process from the modeling of requirements through logical design Steps I and II below.
We illustrate the result of each step of the life cycle with a series of diagrams in Figure 1. Each diagram shows a possible form of the output of each step so the reader can see the progression of the design process from an idea to an actual database implementation.
These forms are discussed in much more detail in Chapters 2—6. Requirements analysis. The database requirements are determined by interviewing both the producers and users of data and using the information to produce a formal requirements specification. That specification includes the data required for processing, the natural data relationships, and the software platform for the database implementation.
As an example, Figure 1. Logical design. The global schema , a conceptual data model diagram that shows all the data and their relationships, is developed using techniques such as entity-relationship ER or UML.
The data model constructs must be ultimately transformed into tables. Conceptual data modeling. The data requirements are analyzed and modeled by using an ER or UML diagram 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 occurrence.
Figure 1. View integration. Usually, when the design is large and more than one person is involved in requirements analysis, multiple views of data and relationships occur, resulting in inconsistencies due to variance in taxonomy, context, or perception.
To eliminate redundancy and inconsistency from the model, these views must be rationalized and consolidated. Pular no carrossel. Anterior no carrossel. Explorar E-books. Os mais vendidos Escolhas dos editores Todos os e-books. Explorar Audiolivros. Os mais vendidos Escolhas dos editores Todos os audiolivros. Explorar Revistas. Escolhas dos editores Todas as revistas. Explorar Podcasts Todos os podcasts. Explorar Documentos. Cancele quando quiser.
Comece seus 30 dias gratuitos Ler a amostra. Editora: Elsevier Science. ISBN: Formato: Livro. In-depth detail and plenty of real-world, practical examples throughout Loaded with design rules and illustrative case studies that are applicable to any SQL, UML, or XML-based system Immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data.
Bancos de Dados. Sobre o autor TT. Ler mais. Livros relacionados. Fundamental of Database Management System de Dr. The Database Giveth and Taketh Away: How many of you have ever run a database query with the wrong parameters and produced a report that you sent to fulfill a request?
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In-depth detail and plenty of real-world, practical examples throughout Loaded with design rules and illustrative case studies that are applicable to any SQL, UML, or XML-based system Immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data.
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Lecture notes and a solutions manual are also available. Chapter 1 Introduction 1. Toby Teorey Toby J. He received his B. His work includes numerous topics in autonomic computing and relational database management systems. He is an IBM Master Inventor with over 25 patents and patents pending; he has published widely on autonomic computing for relational database systems.
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