半结构化数据库设计：Web信息系统的基石

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"《半结构化数据库设计》是Springer出版社发行的一本书，属于Web Information Systems Engineering and Internet Technologies系列，由Yanchun Zhang担任系列编辑。该书详细探讨了半结构化数据库的设计与应用，作者包括Tok Wang Ling、Mong Li Lee和Gillian Dobbie。书中可能涵盖了半结构化数据的特性、查询语言、数据建模方法、以及在Web信息系统的上下文中的实际应用。" 在信息技术领域，半结构化数据库设计是一个关键的话题，尤其是在处理复杂、灵活且不断变化的数据时。传统的关系型数据库对于具有层次或网络结构的数据处理效率不高，而半结构化数据库则能更好地适应这些需求。半结构化数据的特点： 1. 数据结构不固定：半结构化数据允许记录拥有不同数量和类型的字段，这使得数据模型更具灵活性。 2. 层次性：数据通常以树形或图形式组织，可以包含嵌套的数据结构。 3. 自描述性：数据本身包含了关于其结构的信息，如XML文档中的元素和属性。在《半结构化数据库设计》一书中，可能涵盖了以下内容： 1. 数据建模：介绍如何使用如UML（统一建模语言）、XML Schema等工具来描述和定义半结构化数据的模式。 2. 查询语言：讨论XPath、XQuery等针对XML数据的查询语言，以及如何高效地执行这些查询。 3. 存储与索引：探讨如何优化半结构化数据的存储和检索，包括各种索引技术。 4. 数据转换和集成：讲述如何在不同半结构化数据源之间进行数据交换和整合。 5. 数据管理：涵盖数据的完整性、安全性和并发控制等管理问题。 6. 应用场景：通过实例分析半结构化数据库在Web服务、电子商务、社交网络等领域的应用。此书对于理解半结构化数据库的概念、设计原则和实现技术具有重要价值，无论是对数据库研究人员还是从事相关开发的工程师，都能从中获得深入的理论知识和实践经验。通过学习，读者能够掌握处理复杂、非传统数据类型的能力，从而更好地应对互联网时代的信息挑战。

Preface

About This Book

The work presented in this book came about after we recognized that ill-

designed semistructured databases can lead to update anomalies, and there is a

strong need for algorithms and tools to help users design storage structures for

semistructured data. We have been publishing papers in the design of databases

for semistructured data since 1999, and believe that after a number of attempts

we have defined a data model that captures the necessary semantics for repre-

senting the semantics that are necessary in the design of good semistructured

databases.

This book describes a process that initially takes a hardline approach against

redundant data, and then relaxes the approach for gains in query performance.

The book is suited to both researchers and practitioners in the field of semistruc-

tured database design.

Some of the material in this book has been published at international con-

ferences. The material in Chapter 5 was originally based on work presented

in [Wu et al., 2001a] and Chapter 6 was originally based on [Chen et al., 2002].

The material in Chapter 3 was published as a technical report at the National

University of Singapore [Dobbie et al., 2000].

Use of the Book

The target audience of this book is practitioners who design semistructured

data file organizations or semistructured databases, researchers who work in

the area of semistructured data organization, and students with an interest in

the design of storage organizations for semistructured data. The material is as

relevant for file organizations as it is for databases since inconsistencies can

also exist in data files.

Yet another problem that arises from the use of database systems is how

should the constraints from the real world be captured during the design pro-

cess. Typically they are recorded in a conceptual model such as an Entity-

Relationship diagram. Such constraints contain semantic information, that

is, they provide some meaning to the underlying data. It is important that

these constraints are enforced by the database. When data is manipulated,

the database system checks that none of the constraints are violated. In other

words, the semantics from the real world still hold in the result of the manipu-

lation.

Traditional relational databases which assume that data is structured are no

longer suitable for the new Web applications because the data on which the

Web applications are based lacks structure and may be incomplete. Thus, many

of the techniques that were previously used may not be applicable. This less

structure

data, also known as semistructured data, is usually represented as a

tree of elements, where the children are sub-elements of their parent element.

Elements can in turn have attributes. Queries over the trees are represented as

path expressions.

The eXtensible Markup Language (XML) [Bray et al., 2000] is a language

that is used to express semistructured data. XML is self-describing since each

element has a tag which gives a name for the content. However, recently, vari-

ous schema languages have been defined to specify the structure of the underly-

ing XML data and constraints that are expected to hold in instances of the XML

data. The schemas are descriptive rather than prescriptive. Like traditional

data, XML data may be stored in files or in a database. The database can have

an underlying relational engine or it can be specifically designed for XML data.

The former are called XML-enabled databases and the latter are called native

XML databases. Like the entity relationship diagram for relational databases,

a diagrammatic representation that reflects real world constraints could be used

for requirements gathering, and for the design of schemas for semistructured

documents.

Information integration is an important area that has been revisited with

the introduction of XML. It is important that the meaning of the underlying

documents is reflected in the resulting integrated document. Since much of

the meaning can be captured in constraints, the constraints on the underlying

documents should also be enforced in the resulting document. If the semantics

of the underlying documents and the semantics of the resulting document can

be modeled using a diagrammatic representation that reflects the constraints,

then it is possible to check that information is not lost during the process of

integration.

In this book, we investigate the semantics that need to be captured for

semistructured data, and the different ways of representing the semantics. The

rest of the book is organized as follows. Chapter 2 introduces and evaluates the

Introduction

various data models that have been proposed for semistructured data. Chap-

ter 3 gives a more detailed description of one of the data models, ORA-SS

(Object-Relationship-Attribute data model for SemiStructured data). Chapter 4

investigates schema extraction, examining the constraints that can be extracted

from an instance of semistructured data. Algorithms for removing redundancy

from a semistructured data instance are presented in Chapter 5. This is ac-

complished by specifying the structure of the schema in such a way that the

semantics of the data is taken into account. Without knowing the constraints

on the data, it is easy to define views that have no meaning with respect to the

real world. Chapter 6 examines the design of views over semistructured data

and the validity of the views, that is, whether the views designed are consis-

tent with respect to the semantics in the underlying data. Chapter 7 discusses

the physical storage of semistructured data, and investigates the relaxation of

some of the rules presented in Chapter 5 in order to improve performance of

the resulting data store. Finally, we conclude in Chapter 8 with directions for

future research.

1.1

Chapter Overview

The aim of this book is to describe how semantic constraints can be modeled

and used in the design of semistructured databases. The target audience is prac-

titioners who design semistructured data file organizations or semistructured

databases, and researchers who work in the area of semistructured data orga-

nization. The material is as relevant for file organizations as it is for databases

since inconsistencies can also exist in data files. In this section, we give a

preview the materials presented in the subsequent chapters.

Data Models for Semistructured Data

Traditional data models capture constraints such as key constraints, foreign

key constraints, functional dependencies, uniqueness constraints, cardinality

constraints and participation constraints, when modeling data from the real

world. The constraints captured in the data models are used in the design of

databases. Data models for semistructured data initially captured information

that is important for information integration. More recently richer data models

for semistructured data have been defined for data management.

Data models such as OEM [McHugh et al., 1997], DOM [Apparao and

Byrne, 1998], and DataGuides [Goldman and Widom, 1997] have been de-

signed for the express purpose of information integration and finding a com-

mon schema of two or more information sources. The focus of these data

models is on modeling the nested structure of semistructured data, and not on

modeling the constraints that hold in the data. In contrast, data models such

as S3-graphs [Lee et al., 1999], CM Hypergraphs [Embley and Mok, 2001],

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