Database Modeling for Industrial Data Management Emerging Technologies and Applications Zongmin Ma

Database Modeling for Industrial Data Management
Emerging Technologies and Applications Zongmin
Ma pdf download
https://ebookgate.com/product/database-modeling-for-industrial-
data-management-emerging-technologies-and-applications-zongmin-
ma/
Get Instant Ebook Downloads – Browse at https://ebookgate.com

Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
Encyclopedia of Database Technologies and Applications
Laura C. Rivero
https://ebookgate.com/product/encyclopedia-of-database-
technologies-and-applications-laura-c-rivero/
Statistical Modeling and Analysis for Database
Marketing Effective Techniques for Mining Big Data 1st
Edition Bruce Ratner
https://ebookgate.com/product/statistical-modeling-and-analysis-
for-database-marketing-effective-techniques-for-mining-big-
data-1st-edition-bruce-ratner/
Database Technologies Concepts Methodologies Tools and
Applications 1st Edition John Erickson
https://ebookgate.com/product/database-technologies-concepts-
methodologies-tools-and-applications-1st-edition-john-erickson/
Advanced Database Query Systems Techniques Applications
and Technologies 1st Edition Li Yan
https://ebookgate.com/product/advanced-database-query-systems-
techniques-applications-and-technologies-1st-edition-li-yan/

MPLS Enabled Applications Emerging Developments and New
Technologies Second Edition Ms Ina Minei
https://ebookgate.com/product/mpls-enabled-applications-emerging-
developments-and-new-technologies-second-edition-ms-ina-minei/
Quantifying and Understanding Plant Nitrogen Uptake for
Systems Modeling 1st Edition Liwang Ma
https://ebookgate.com/product/quantifying-and-understanding-
plant-nitrogen-uptake-for-systems-modeling-1st-edition-liwang-ma/
Ecological Technologies for Industrial Wastewater
Management Petrochemicals Metals Semi Conductors and
Paper Industries 1st Edition Victor M. Monsalvo
(Editor)
https://ebookgate.com/product/ecological-technologies-for-
industrial-wastewater-management-petrochemicals-metals-semi-
conductors-and-paper-industries-1st-edition-victor-m-monsalvo-
editor/
Biological Database Modeling 1st Edition Jake Chen
https://ebookgate.com/product/biological-database-modeling-1st-
edition-jake-chen/
Emerging Technologies for Food Processing Second
Edition Da-Wen Sun
https://ebookgate.com/product/emerging-technologies-for-food-
processing-second-edition-da-wen-sun/

Hershey • London • Melbourne • Singapore
IDEA GROUP PUBLISHING
Database Modeling
for Industrial
Data Management:
Emerging Technologies
and Applications
ZongminMa
NortheasternUniversity,China

Acquisitions Editor: Michelle Potter
Development Editor: Kristin Roth
SeniorManagingEditor: Amanda Appicello
ManagingEditor: JenniferNeidig
Copy Editor: SusannaSvidunovich
Typesetter: AmandaKirlin
CoverDesign: Lisa Tosheff
Printed at: Integrated Book Technology
Published in the United States of America by
Idea Group Publishing (an imprint of Idea Group Inc.)
701 E. Chocolate Avenue
Hershey PA 17033
Tel: 717-533-8845
Fax: 717-533-8661
E-mail:cust@idea-group.com
Web site: http://www.idea-group.com
and in the United Kingdom by
Idea Group Publishing (an imprint of Idea Group Inc.)
3 Henrietta Street
Covent Garden
London WC2E 8LU
Tel: 44 20 7240 0856
Fax: 44 20 7379 0609
Web site: http://www.eurospanonline.com
Copyright © 2006 by Idea Group Inc. All rights reserved. No part of this book may be repro-
duced, stored or distributed in any form or by any means, electronic or mechanical, including
photocopying, without written permission from the publisher.
Product or company names used in this book are for identification purposes only. Inclusion of the
names of the products or companies does not indicate a claim of ownership by IGI of the
trademark or registered trademark.
Library of Congress Cataloging-in-Publication Data
Databasemodelingforindustrialdatamanagement:emergingtechnologies
and applications / Zongmin Ma, editor.
p. cm.
Summary: "This book covers industrial databases and applications and
offers generic database modeling techniques"--Provided by publisher.
Includes bibliographical references and index.
ISBN 1-59140-684-6 (hardcover) -- ISBN 1-59140-685-4 (softcover)
-- ISBN 1-59140-686-2 (ebook)
1. Industrial management--Technological innovations. 2. Relational
databases. 3. Database design. I. Ma, Zongmin, 1965- .
HD45.D327 2005
005.75'6--dc22
2005023883
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in
this book are those of the authors, but not necessarily of the publisher.

Database Modeling
for Industrial
Data Management:
Emerging Technologies
and Applications
Table of Contents
Preface .................................................................................................. vi
Acknowledgments ................................................................................ xii
SECTION I: INDUSTRIAL DATABASES AND APPLICATIONS
ChapterI
DatabasesModelingofEngineeringInformation................................ 1
Z. M. Ma, Northeastern University, China
ChapterII
Database Design Based on B ............................................................. 35
Elvira Locuratolo, Consiglio Nazionale delle Ricerche, Italy
ChapterIII
TheManagementofEvolvingEngineeringDesignConstraints....... 62
T. W. Carnduff, University of Glamorgan, UK
J. S. Goonetillake, University of Colombo, Sri Lanka

ChapterIV
SimilaritySearchforVoxelizedCADObjects ................................. 115
Hans-Peter Kriegel, University of Munich, Germany
Peer Kröger, University of Munich, Germany
Martin Pfeifle, University of Munich, Germany
Stefan Brecheisen, University of Munich, Germany
Marco Pötke, software design & management AG, Germany
Matthias Schubert, University of Munich, Germany
Thomas Seidl, RWTH Aachen, Germany
ChapterV
STEP-NCtoCompleteProductDevelopmentChain....................... 148
Xun W. Xu, University of Auckland, New Zealand
ChapterVI
Semantic-BasedDynamicEnterpriseInformationIntegration ....... 185
Jun Yuan, The Boeing Company, USA
ChapterVII
Web Service Integration and Management Strategies
for Large-Scale Datasets .................................................................. 217
Yannis Panagis, University of Patras & Research Academic
Computer Technology Institute, Greece
Evangelos Sakkopoulos, University of Patras & Research Academic
Spyros Sioutas, University of Patras, Greece
Athanasios Tsakalidis, University of Patras & Research Academic
ChapterVIII
Business Data Warehouse: The Case of Wal-Mart........................ 244
Indranil Bose, The University of Hong Kong, Hong Kong
Lam Albert Kar Chun, The University of Hong Kong, Hong Kong
Leung Vivien Wai Yue, The University of Hong Kong, Hong Kong
Li Hoi Wan Ines, The University of Hong Kong, Hong Kong
Wong Oi Ling Helen, The University of Hong Kong, Hong Kong

ChapterIX
A Content-Based Approach to Medical Image
Database Retrieval ........................................................................... 258
Chia-Hung Wei, University of Warwick, UK
Chang-Tsun Li, University of Warwick, UK
Roland Wilson, University of Warwick, UK
SECTION II: GENERIC DATABASE MODELING
ChapterX
Conceptual Modeling for XML: A Myth or a Reality .................... 293
Sriram Mohan, Indiana University, USA
Arijit Sengupta, Wright State University, USA
ChapterXI
Constraint-BasedMulti-DimensionalDatabases ............................ 323
Franck Ravat, Université Toulouse I, France
Olivier Teste, Université Toulouse III, France
Gilles Zurfluh, Université Toulouse I, France
AbouttheAuthors.............................................................................. 361
Index................................................................................................... 369

vi
Preface
Computer-based information technologies have been extensively used to help
industries manage their processes, and information systems hereby become
their nervous center. More specifically, databases are designed to support the
data storage, processing, and retrieval activities related to data management
in information systems. Database management systems provide efficient task
support, and database systems are the key to implementing industrial data
management. Industrial data management requires database technical sup-
port. Industrial applications, however, are typically data- and knowledge-in-
tensive and have some unique characteristics (e.g., large volumes of data with
complex structures) that make them difficult to manage. Some new techniques
such as the Web, artificial intelligence, and so forth have been introduced into
industrial applications. These unique characteristics and the usage of new tech-
nologies have put many potential requirements on industrial data management,
which challenges today’s database systems and promotes their evolvement.
Viewed from database technology, information modeling in databases (data-
base modeling for short) can be identified at two levels: conceptual data mod-
eling and database modeling. This results in conceptual (semantic) data model
and logical database model. Generally, a conceptual data model is designed,
then the designed conceptual data model will be transformed into a chosen
logical database schema. Database systems based on logical database mod-
els are used to build information systems for data management. Much atten-
tion has been directed at conceptual data modeling of industrial information
systems. Product data models, for example, can be viewed as a class of se-
mantic data models (i.e., conceptual data models) that take into account the
needs of engineering data. Recently, conceptual data modeling of enterprises
has received increasing attention. Generally speaking, traditional ER/EER or

vii
UML models in database areas can be used for industrial data modeling at the
conceptual level. But, limited by their power in industrial data modeling, some
new conceptual data models such as IDEF1X and STEP/EXPRESS have
been developed. In particular, to implement share and exchange of industrial
data, the Standard for the Exchange of Product Model Data (STEP) is being
developed by the International Organization for Standardization (ISO). EX-
PRESS is the description methods of STEP and a conceptual schema lan-
guage, which can model product design, manufacturing, and production data.
EXPRESS model hereby becomes a major one of conceptual data models for
industrial data modeling. Many research works have been reported on the
database implementation of the EXPRESS model in context of STEP, and
some software packages and tools are available in the marketplace. For in-
dustrial data modeling in database systems, the generic logical database mod-
els such as relational, nested relational, and object-oriented databases have
been used. However, these generic logical database models do not always
satisfy the requirements of industrial data management. In non-transaction pro-
cessing such as CAD/CAM, knowledge-based system, multimedia and Internet
systems, for example, most of these data-intensive application systems suffer
from the same limitations of relational databases. Some non-traditional data-
base models based on special, hybrid, and/or the extended database models
above have been proposed accordingly.
Database technology is typically application-oriented. With advances and in-
depth applications of computer technologies in industry, database modeling
for industrial data management is emerging as a new discipline. The research
and development of industrial databases is receiving increasing attention. By
means of database technology, large volumes of industrial data with complex
structures can be modeled in conceptual data models and further stored in
databases. Industrial information systems based the databases can handle and
retrieve these data to support various industrial activities. Therefore, database
modeling for industrial data management is a field which must be investigated
by academic researchers, together with developers and users both from data-
base and industry areas.
Introduction
This book, which consists of 11 chapters, is organized into two major sec-
tions. The first section discusses the issues of industrial databases and appli-

viii
cations in the first nine chapters. The next two chapters covering the data
modeling issue in generic databases comprise the second section.
First of all, we take a look at the problems of the industrial databases and
applications.
Databases are designed to support data storage, processing, and retrieval
activities related to data management, and database systems are the key to
implementing engineering information modeling. But some engineering re-
quirements challenge current mainstream databases, which are mainly used
for business applications, and promote their evolvement. Ma tries to identify
the requirements for engineering information modeling and then investigates
the satisfactions of current database models to these requirements at two
levels: conceptual data models and logical database models. Also, the rela-
tionships among the conceptual data models and the logical database models
for engineering information modeling are presented as viewed from database
conceptual design.
ASSO is a database design methodology defined for achieving conceptual
schemaconsistency,logicalschemacorrectness,flexibilityinreflectingthereal-
life changes on the schema, and efficiency in accessing and storing informa-
tion. B is an industrial formal method for specifying, designing, and coding
software systems. Locuratolo investigates the integration of the ASSO fea-
tures in B. Starting from a B specification of the data structure and of the
transactions allowed on a database, two model transformations are designed:
The resulting model Structured Database Schema integrates static and dy-
namics, exploiting the novel concepts of Class-Machines and Specialized
Class-Machines. Formal details which must be specified if the conceptual
model of ASSO is directly constructed in B are avoided; the costs of the
consistency obligations are minimized. Class-Machines supported by seman-
tic data models can be correctly linked with Class-Machines supported by
object models.
Carnduff and Goonetillake present research aimed at determining the require-
ments of a database software tool that supports integrity validation of versioned
design artifacts through effective management of evolving constraints. It re-
sults in the design and development of a constraint management model, which
allows constraint evolution through representing constraints within versioned
objects called Constraint Versions Objects (CVOs). This model operates
around a version model that uses a well-defined configuration management
strategy to manage the versions of complex artifacts. Internal and interdepen-
dency constraints are modeled in CVOs. They develop a model which has
been implemented in a prototype database tool with an intuitive user interface.

ix
The user interface allows designers to manage design constraints without the
need to program. Also, they introduce the innovative concepts developed us-
ing an ongoing example of a simple bicycle design.
Similarity search in database systems is an important task in modern applica-
tiondomainssuchasmultimedia,molecularbiology,medicalimagingandmany
others. Especially for CAD (Computer-Aided Design), suitable similarity
models and a clear representation of the results can help to reduce the cost
of developing and producing new parts by maximizing the reuse of existing
parts. Kriegel, Kröger, Pfeifle, Brecheisen, Pötke, Schubert, and Seidl
present different similarity models for voxelized CAD data based on space
partitioning and data partitioning. Based on these similarity models, they in-
troduce an industrial prototype, called BOSS, which helps the user to get an
overview over a set of CAD objects. BOSS allows the user to easily browse
large data collections by graphically displaying the results of a hierarchical
clusteringalgorithm.
STEP-NC is an emerging ISO standard, which defines a new generation of
NC programming language and is fully compliant with STEP. There is a whole
suite of implementation methods one may utilize for development purposes.
STEP-NCbringsricherinformationtothenumerically-controlledmachinetools;
hence intelligent machining and control are made possible. Its Web-enabled
featuregivesitselfanadditionaldimensioninthate-manufacturingcanbereadily
supported. Xu addresses the issue of product development chain from the
perspective of data modeling and streamlining. The focus is on STEP-NC,
and how it may close the gap between design and manufacturing for a com-
plete, integrated product development environment. A case study is given to
demonstrate a STEP compliant, Web-enabled manufacturing system.
Yuan shares his experience of enabling semantic-based dynamic information
integration across multiple heterogeneous information sources. While data is
physically stored in existing legacy data systems across the networks, the in-
formation is integrated based upon its semantic meanings. Ontology is used to
describe the semantics of global information content, and semantic enhance-
ment is achieved by mapping the local metadata onto the ontology. For better
system reliability, a unique mechanism is introduced to perform appropriate
adjustments upon detecting environmental changes.
Panagis, Sakkopoulos, Sioutas, and Tsakalidis present the Web Service ar-
chitecture and propose Web Service integration and management strategies
for large-scale datasets. They mainly present the elements of Web Service
architecture, the challenges in implementing Web Services whenever large-
scale data are involved, and the design decisions and business process re-

x
engineering steps to integrate Web Services in an enterprise information sys-
tem. Then they provide a case study involving the largest private-sector tele-
phony provider in Greece, where the provider’s billing system datasets is uti-
lized. Moreover, they present the scientific work on Web Service discovery
along with experiments on implementing an elaborate discovery strategy over
real-world, large-scale data.
Bose, Chun, Yue, Ines, and Helen describe the planning and implementation
of the Wal-Mart data warehouse and discuss its integration with the opera-
tional systems. They also highlight some of the problems encountered in the
developmental process of the data warehouse. The implications of the recent
advances in technologies such as RFID, which is likely to play an important
role in the Wal-Mart data warehouse in future, is also detailed.
Content-based image retrieval (CBIR) can be used to locate medical images
in large databases using image features, such as color and texture, to index
images with minimal human intervention. Wei, Li, and Wilson introduce a con-
tent-based approach to medical image retrieval. First, they introduce the fun-
damentals of the key components of content-based image retrieval systems
are to give an overview of this area. Then they present a case study, which
describes the methodology of a CBIR system for retrieving digital mammo-
gram database.
In the second section, we see the generic database modeling.
A strong design phase is involved in most current application development
processes (e.g., ER design for relational databases). But conceptual design
for XML has not been explored significantly in literature or in practice. Most
XML design processes start by directly marking up data in XML, and the
metadata is typically designed at the time of encoding the documents. So
Mohan and Sengupta introduce the existing methodologiesformodelingXML.
A discussion is presented comparing and contrasting their capabilities and
deficiencies, and delineating the future trend in conceptual design for XML
applications.
Ravat, Teste, and Zurfluh focus on constraint-based multi-dimensional mod-
eling. The defined model integrates a constellation of facts and dimensions.
Along each dimension, various hierarchies are possibly defined and the model
supports multiple instantiations of dimensions. To facilitate data querying, they
also define a multi-dimensional query algebra, which integrates the main multi-
dimensional operators. These operators support the constraint-based multi-
dimensional modeling. Finally, they present two implementations of this alge-
bra, which are OLAP-SQL and a graphical query language. The former is a
textual language integrating multi-dimensional concepts (fact, dimension, hier-

xi
archy), but it is based on classical SQL syntax. This language is dedicated to
specialists such as multi-dimensional database administrators. The latter con-
sists in a graphical representation of multi-dimensional databases and users
specify directly their queries over this graph. This approach is dedicated to
non-computer scientist users.

xii
Acknowledgments
The editor wishes to thank all of the authors for their insights and
excellent contributions to this book, and would like to acknowl-
edge the help of all involved in the collation and review process of
the book, without whose support the project could not have been
satisfactorily completed. Most of the authors of chapters included
in this book also served as referees for papers written by other
authors. Thanks go to all those who provided constructive and
comprehensive reviews.
A further special note of thanks goes also to all the staff at Idea
Group Inc., whose contributions throughout the whole process
from inception of the initial idea to final publication have been
invaluable. Special thanks also go to the publishing team at Idea
Group Inc. — in particular to Mehdi Khosrow-Pour, whose en-
thusiasm motivated me to initially accept his invitation for taking
on this project, and to Michele Rossi, who continuously prodded
via e-mail for keeping the project on schedule. This book would
not have been possible without the ongoing professional support
from Mehdi Khosrow-Pour and Jan Travers at Idea Group Inc.
The idea of editing this volume stems from the initial research
work that the editor did in the past several years. The assistances
and facilities of University of Saskatchewan and Université de
Sherbrooke, Canada, Oakland University and Wayne State Uni-
versity, USA, and City University of Hong Kong and North-
eastern University, China, are deemed important, and are highly
appreciated.

Finally, the editor wishes to thank his family for their patience,
understanding, encouragement, and support when the editor
needed to devote many time in the edition of this book. This book
will not be completed without their love.
Zongmin Ma, PhD
Shenyang, China
May 2005
xiii

SECTION I:
INDUSTRIAL DATABASES
AND APPLICATIONS

Databases Modeling of Engineering Information 1
Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written
permission of Idea Group Inc. is prohibited.
Chapter I
DatabasesModeling
ofEngineering
Information
Z. M. Ma, Northeastern University, China
Abstract
Information systems have become the nerve center of current computer-
based engineering applications, which hereby put the requirements on
engineering information modeling. Databases are designed to support data
storage, processing, and retrieval activities related to data management,
and database systems are the key to implementing engineering information
modeling. It should be noted that, however, the current mainstream
databasesaremainlyusedforbusinessapplications.Somenewengineering
requirements challenge today’s database technologies and promote their
evolvement.Databasemodelingcanbeclassifiedintotwolevels:conceptual
data modeling and logical database modeling. In this chapter, we try to
identify the requirements for engineering information modeling and then
investigatethesatisfactionsofcurrentdatabasemodelstotheserequirements
at two levels: conceptual data models and logical database models. In
addition, the relationships among the conceptual data models and the
logicaldatabasemodelsforengineeringinformationmodelingarepresented
in the chapter viewed from database conceptual design.

2 Ma
Introduction
Toincreaseproductcompetitiveness,currentmanufacturingenterpriseshave
to deliver their products at reduced cost and high quality in a short time. The
changefromsellers’markettobuyers’marketresultsinasteadydecreasein
the product life cycle time and the demands for tailor-made and small-batch
products. All these changes require that manufacturing enterprises quickly
respondtomarketchanges.Traditionalproductionpatternsandmanufacturing
technologiesmayfinditdifficulttosatisfytherequirementsofcurrentproduct
development. Many types of advanced manufacturing techniques, such as
ComputerIntegratedManufacturing(CIM),AgileManufacturing(AM),Con-
currentEngineering(CE),andVirtualEnterprise(VE)basedonglobalmanu-
facturinghavebeenproposedtomeettheserequirements.Oneofthefounda-
tional supporting strategies is the computer-based information technology.
Informationsystemshavebecomethenervecenterofcurrentmanufacturing
systems.Sosomenewrequirementsoninformationmodelingareintroduced.
Databasesystemsarethekeytoimplementinginformationmodeling.Engineer-
inginformationmodelingrequiresdatabasesupport.Engineeringapplications,
however, are data- and knowledge- intensive applications. Some unique
characteristicsandusageofnewtechnologieshaveputmanypotentialrequire-
mentsonengineeringinformationmodeling,whichchallengetoday’sdatabase
systemsandpromotetheirevolvement.Databasesystemshavegonethrough
thedevelopmentfromhierarchicalandnetworkdatabasestorelationaldata-
bases. But in non-transaction processing such as CAD/CAPP/CAM (com-
puter-aideddesign/computer-aidedprocessplanning/computer-aidedmanu-
facturing),knowledge-basedsystem,multimediaandInternetsystems,mostof
thesedata-intensiveapplicationsystemssufferfromthesamelimitationsof
relationaldatabases.Therefore,somenon-traditionaldatamodelshavebeen
proposed.Thesedatamodelsarefundamentaltoolsformodelingdatabasesor
the potential database models. Incorporation between additional semantics
anddatamodelshasbeenamajorgoalfordatabaseresearchanddevelopment.
Focusingonengineeringapplicationsofdatabases,inthischapter,weidentify
the requirements for engineering information modeling and investigate the
satisfactions of current database models to these requirements. Here we
differentiatetwolevelsofdatabasemodels:conceptualdatamodelsandlogical
databasemodels.Constructionsofdatabasemodelsforengineeringinforma-
tionmodelingareherebyproposed.

Theremainderofthechapterisorganizedasfollows:Thenextsectionidentifies
thegenericrequirementsofengineeringinformationmodeling.Theissuesthat
currentdatabasessatisfytheserequirementsaretheninvestigatedinthethird
section. The fourth section proposes the constructions of database models.
Thefinalsectionconcludesthischapter.
Needs for
Engineering Information Modeling
Complex Objects and Relationships
Engineeringdatahavecomplexstructuresandareusuallylargeinvolume.But
engineering design objects and their components are not independent. In
particular, they are generally organized into taxonomical hierarchies. The
specializationassociationisthewell-knownassociation.Alsothepart-whole
association,whichrelatescomponentstothecompoundofwhichtheyarepart,
isanotherkeyassociationinengineeringsettings.
In addition, the position relationships between the components of design
objectsandtheconfigurationinformationaretypicallymulti-dimensional.Also,
theinformationofversionevolutionisobviouslytime-related.Allthesekinds
ofinformationshouldbestored.Itisclearthatspatio-temporaldatamodeling
isessentialinengineeringdesign(Manwaring,Jones,&Glagowski,1996).
Typically, product modeling for product family and product variants has
resultedinproductdatamodels,whichdefinetheformandcontentofproduct
datageneratedthroughtheproductlifecyclefromspecificationthroughdesign
tomanufacturing.Productsaregenerallycomplex(seeFigure1,whichshows
asimpleexampleofproductstructure)andproductdatamodelsshouldhereby
have advanced modeling abilities for unstructured objects, relationships,
abstractions, and so on (Shaw, Bloor, & de Pennington, 1989).
Data Exchange and Share
Engineeringactivitiesaregenerallyperformedacrossdepartmentalandorga-
nization boundaries. Product development based on virtual enterprises, for

4 Ma
example,isgenerallyperformedbyseveralindependentmembercompanies
thatarephysicallylocatedatdifferentplaces.Informationexchangeandshare
among them is necessary. It is also true in different departments or even in
differentgroupswithinamembercompany.Enterpriseinformationsystems
(EISs)inmanufacturingindustry,forexample,typicallyconsistofsupplychain
management (SCM), enterprise resource planning (ERP) (Ho, Wu, & Tai,
2004), and CAD/CAPP/CAM. These individual software systems need to
shareandexchangeproductandproductioninformationinordertoeffectively
organize production activities of enterprise. However, they are generally
developedindependently.Insuchanenvironmentofdistributedandheteroge-
neouscomputer-basedsystems,exchangingandsharingdataacrossunitsare
very difficult. An effective means must be provided so that the data can be
exchangedandsharedamongdeferentapplicationsandenterprises.Recently,
the PDM (product data management) system (CIMdata, 1997) is being
extensively used to integrate both the engineering data and the product
development process throughout the product lifecycle, although the PDM
systemalsohastheproblemofexchangingdatawithERP.
Web-Based Applications
Informationsystemsintoday’smanufacturingenterprisesaredistributed.Data
exchange and share can be performed by computer network systems. The
Internetisalargeandconnectednetworkofcomputers,andtheWorldWide
Web (WWW) is the fastest growing segment of the Internet. Enterprise
operationsgoincreasinglyglobal,andWeb-basedmanufacturingenterprises
Figure 1. An example illustration of product structure
Part 1 Part 2 … Part m
Bought Part
Turned Part
Forged Part
Assembly Part
Part-whole association Specialization association
Manufactured Part
Product

cannotonlyobtainonlineinformationbutalsoorganizeproductionactivities.
Web technology facilitates cross-enterprise information sharing through
interconnectivityandintegration,whichcanconnectenterprisestotheirstrate-
gic partners as well as to their customers. So Web-based virtual enterprises
(Zhang, Zhang, & Wang, 2000), Web-based PDM (Chu & Fan, 1999; Liu &
Xu, 2001), Web-based concurrent engineering (Xue & Xu, 2003), Web-
based supply chain management, and Web-based B2B e-commerce for
manufacturing(Fenseletal.,2001;Shaw,2000a,2000b;Soliman&Youssef,
2003;Tan,Shaw,&Fulkerson,2000)areemerging.Acomprehensivereview
wasgivenofrecentresearchondevelopingWeb-basedmanufacturingsystems
in Yang and Xue (2003).
The data resources stored on the Web are very rich. In addition to common
typesofdata,therearemanyspecialtypesofdatasuchasmultimediadataand
hypertextlink,whicharereferredtoassemi-structureddata.Withtherecent
popularityoftheWWWandinformativemanufacturingenterprises,howto
model and manipulate semi-structured data coming from various sources in
manufacturingdatabasesisbecomingmoreandmoreimportant.Web-based
applications, including Web-based supply chain management, B2B e-com-
merce,andPDMsystems,havebeenevolvedfrominformationpublicationto
informationshareandexchange.HTML-basedWebapplicationcannotsatisfy
suchrequirements.
Intelligence for Engineering
Artificialintelligenceandexpertsystemshaveextensivelybeenusedinmany
engineeringactivitiessuchasproductdesign,manufacturing,assembly,fault
diagnosis,andproductionmanagement.Fiveartificialintelligencetoolsthatare
most applicable to engineering problems were reviewed in Pham and Pham
(1999), which are knowledge-based systems, fuzzy logic, inductive learn-
ing, neural networks, and genetic algorithms. Each of these tools was
outlinedinthepapertogetherwithexamplesoftheiruseindifferentbranches
ofengineering.InIssa,Shen,andChew(1994),anexpertsystemthatapplies
analogical reasoning to mechanism design was developed. Based on fuzzy
logic, an integration of financial and strategic justification approaches was
proposedformanufacturinginChiadamrong(1999).

6 Ma
Imprecision and Uncertainty
Imprecision is most notable in the early phase of the design process and has
been defined as the choice between alternatives (Antonsoon & Otto, 1995).
Four sources of imprecision found in engineering design were classified as
relationship imprecision, data imprecision, linguistic imprecision, and
inconsistencyimprecisioninGiachettietal.(1997).Inadditiontoengineering
design,impreciseanduncertaininformationcanbefoundinmanyengineering
activities. The imprecision and uncertainty in activity control for product
developmentwasinvestigatedinGrabotandGeneste(1998).Tomanagethe
uncertainty occurring in industrial firms, the various types of buffers were
provided in Caputo (1996) according to different types of uncertainty faced
and to the characteristics of the production system. Buffers are used as
alternativeandcomplementaryfactorstoattaintechnologicalflexibilitywhena
firmisunabletoachievethedesiredlevelofflexibilityandfacesuncertainty.
Nine types of flexibility (machine, routing, material handling system,
product,operation, process, volume, expansion, and labor) in manufactur-
ingweresummarizedinTsourveloudisandPhillis(1998).
Concerningtherepresentationofimprecisionanduncertainty,attemptshave
beenmadetoaddresstheissueofimprecisionandinconsistencyindesignby
wayofintervals(Kimetal.,1995).Otherapproachestorepresentingimpre-
cision in design include using utility theory, implicit representations using
optimizationmethods,matrixmethodssuchasQualityFunctionDeployment,
probability methods, and necessity methods. An extensive review of these
approacheswasprovidedinAntonsoonandOtto(1995).Thesemethodshave
allhadlimitedsuccessinsolvingdesignproblemswithimprecision.Itisbelieved
thatfuzzyreorientationofimprecisionwillplayanincreasinglyimportantrolein
designsystems(Zimmermann,1999).
Fuzzy set theory (Zadeh, 1965) is a generalization of classical set theory. In
normal set theory, an object may or may not be a member of a set. There are
only two states. Fuzzy sets contain elements to a certain degree. Thus, it is
possible to represent an object that has partial membership in a set. The
membership value of element u in a fuzzy set is represented by µ(u) and is
normalizedsuchthatµ(u)isin[0,1].Formally,letFbeafuzzysetinauniverse
ofdiscourseUandµF
:U→[0,1]bethemembershipfunctionforthefuzzyset
F. Then the fuzzy set F is described as:

F = {µ(u1
)/u1
, µ(u2
)/u2
, ..., µ(un
)/un
}, where ui
∈ U(i = 1, 2, …, n).
Fuzzy sets can represent linguistic terms and imprecise quantities and make
systems more flexible and robust. So fuzzy set theory has been used in some
engineeringapplications(e.g.,engineering/productdesignandmanufacturing,
productionmanagement,manufacturingflexibility,e-manufacturing,etc.),where,
eithercrispinformationisnotavailableorinformationflexibleprocessingis
necessary.
1. Concerningengineering/productdesignandmanufacturing,theneedsfor
fuzzylogicinthedevelopmentofCADsystemswereidentifiedandhow
fuzzy logic could be used to model aesthetic factors was discussed in
Pham(1998).Thedevelopmentofanexpertsystemwithproductionrules
andtheintegrationoffuzzytechniques(fuzzyrulesandfuzzydatacalculus)
wasdescribedforthepreliminarydesigninFrancoisandBigeon(1995).
Integratingknowledge-basedmethodswithmulti-criteriadecision-mak-
ingandfuzzylogic,anapproachtoengineeringdesignandconfiguration
problemswasdevelopedinordertoenrichexistingdesignandconfigu-
rationsupportsystemswithmoreintelligentabilitiesinMullerandSebastian
(1997).Amethodologyformakingthetransitionfromimprecisegoalsand
requirements to the precise specifications needed to manufacture the
productwasintroducedusingfuzzysettheoryinGiachettietal.(1997).
In Jones and Hua (1998), an approach to engineering design in which
fuzzy sets were used to represent the range of variants on existing
mechanisms was described so that novel requirements of engineering
design could be met. A method for design candidate evaluation and
identificationusingneuralnetwork-basedfuzzyreasoningwaspresented
in Sun, Kalenchuk, Xue, and Gu (2000).
2. Inproductionmanagement,thepotentialapplicationsoffuzzysettheory
to new product development; facility location and layout; production
scheduling and control; inventory management; and quality and cost-
benefit analysis were identified in Karwowski and Evans (1986). A
comprehensive literature survey on fuzzy set applications in product
managementresearchwasgiveninGuiffridaandNagi(1998).Aclassi-
ficationschemeforfuzzyapplicationsinproductmanagementresearch
wasdefinedintheirpaper,includingjobshopscheduling;qualitymanage-
ment;projectscheduling;facilitieslocationandlayout;aggregateplan-
ning;productionandinventoryplanning;andforecasting.

8 Ma
3. Inmanufacturingdomain,flexibilityisaninherentlyvaguenotion.Sofuzzy
logic was introduced and a fuzzy knowledge-based approach was used
tomeasuremanufacturingflexibility(Tsourveloudis&Phillis,1998).
4. Morerecently,theresearchonsupplychainmanagementandelectronic
commerce have also shown that fuzzy set can be used in customer
demand, supply deliveries along the supply chain, external or market
supply,targetedmarketing,andproductcategorydescription(Petrovic,
Roy, & Petrovic, 1998, 1999; Yager, 2000; Yager & Pasi, 2001).
It is believed that fuzzy set theory has considerable potential for intelligent
manufacturingsystemsandwillbeemployedinmoreandmoreengineering
applications.
Knowledge Management
Engineeringapplicationisaknowledge-intensiveapplication.Knowledge-based
managementshavecoveredthewholeactivitiesofcurrententerprises(O’Leary,
1998;Maedcheetal.,2003;Wong,2005),includingmanufacturingenterprises
(Michael&Khemani,2002).InTanandPlatts(2004),theuseoftheconnectance
conceptformanagingmanufacturingknowledgewasproposed.Asoftwaretool
calledToolforActionPlanSelection(TAPS)hasbeendevelopedbasedonthe
connectance concept, which enables managers to sketch and visualize their
knowledgeofhowvariablesinteractinaconnectancenetwork.Basedonthe
computer-integratedmanufacturingopen-systemarchitecturereferencemodel
(CIMOSA),aformalismwaspresentedindeSouza,Ying,andYang(1998)to
specifythebusinessprocessesandenterpriseactivitiesattheknowledgelevel.
Theformalismusedanintegrationofmultipletypesofknowledge,including
precise,muddy,andrandomsymbolicandnumericalknowledgetosystemati-
cally represent enterprise behavior and functionality. Instead of focusing on
individualhumanknowledge,asinThannhuber,Tseng,andBullinger(2001),the
abilityofanenterprisetodynamicallyderiveprocessestomeettheexternalneeds
andinternalstabilitywasidentifiedastheorganizationalknowledge.Onthebasis,
aknowledgemanagementsystemhasbeendeveloped.
Themanagementofengineeringknowledgeentailsitsmodeling,maintenance,
integration,anduse(Ma&Mili,2003;Milietal.,2001).Knowledgemodeling
consistsofrepresentingtheknowledgeinsomeselectedlanguageornotation.
Knowledgemaintenanceencompassesallactivitiesrelatedtothevalidation,

growth,andevolutionoftheknowledge.Knowledgeintegrationisthesynthesis
ofknowledgefromrelatedsources.Theuseoftheknowledgerequiresbridging
thegapbetweentheobjectiveexpressedbytheknowledgeandthedirectives
neededtosupportengineeringactivities.
ItshouldbenoticedthatWeb-basedengineeringknowledgemanagementhas
emerged because of Web-based engineering applications (Caldwell et al.,
2000). In addition, engineering knowledge is closely related to engineering
data,althoughtheyaredifferent.Engineeringknowledgeisgenerallyembed-
dedinengineeringdata.Soitisnecessarytosyntheticallymanageengineering
knowledge and data in bases (Xue, Yadav, & Norrie, 1999; Zhang & Xue,
2002).Finally,thefieldofartificialintelligence(AI)isusuallyconcernedwith
theproblemscausedbyimpreciseanduncertaininformation(Parsons,1996).
Knowledge representation is one of the most basic and active research areas
ofAI.Theconventionalapproachestoknowledgerepresentation,however,
onlysupportexactratherthanapproximatereasoning,andfuzzylogicisaptfor
knowledgerepresentation(Zadeh,1989).Fuzzyrules(Dubois&Prade,1996)
andfuzzyconstraints(Dubois,Fargier,&Prade,1996)havebeenadvocated
andemployedasakeytoolforexpressingpiecesofknowledgeinfuzzylogic.
In particular, fuzzy constraint satisfaction problem (FCSP) has been used in
many engineering activities such as design and optimization (Dzbor, 1999;
Kapadia & Fromherz, 1997; Young, Giachetti, & Ress, 1996) as well as
planningandscheduling(Dubois,Fargier,&Prade,1995;Fargier&Thierry,
1999; Johtela et al., 1999).
Data Mining and Knowledge Discovery
Engineering knowledge plays a crucial role in engineering activities. But
engineeringknowledgeisnotalwaysrepresentedexplicitly.Dataminingand
knowledgediscoveryfromdatabases(KDD)canextractinformationcharac-
terized as “knowledge” from data that can be very complex and in large
quantities.Sothefieldofdataminingandknowledgediscoveryfromdatabases
hasemergedasanewdisciplineinengineering(Gertosio&Dussauchoy,2004)
and now is extensively studied and applied in many industrial processes. In
Ben-Arieh,Chopra,andBleyberg(1998),dataminingapplicationforreal-time
distributedshop-floorcontrolwaspresented.Withadataminingapproach,the
predictionproblemencounteredinengineeringdesignwassolvedinKusiak
andTseng(2000).Furthermore,thedataminingissuesandrequirementswithin
anenterprisewereexaminedinKleissner(1998).

10 Ma
Withthehugeamountofinformationavailableonline,theWorldWideWebis
a fertile area for data mining research. The Web mining research is at the
crossroadsofresearchfromseveralresearchcommunitiessuchasdatabase,
information retrieval, and within AI, especially the sub-areas of machine
learning and natural language processing (Kosala & Blockeel, 2000). In
addition,softcomputingmethodologies(involvingfuzzysets,neuralnetworks,
geneticalgorithms,androughsets)aremostwidelyappliedinthedatamining
step of the overall KDD process (Mitra, Pal, & Mitra, 2002). Fuzzy sets
provideanaturalframeworkfortheprocessindealingwithuncertainty.Neural
networksandroughsetsarewidelyusedforclassificationandrulegeneration.
Genetic algorithms (GAs) are involved in various optimization and search
processes, like query optimization and template selection. Particularly, a
reviewofWebMininginSoftComputingFrameworkwasgiveninPal,Talwar,
and Mitra (2002).
Current Database Models
Engineering information modeling in databases can be carried out at two
different levels: conceptual data modeling and logical database modeling.
Therefore, we have conceptual data models and logical database models for
engineering information modeling, respectively. In this chapter, database
modelsforengineeringinformationmodelingrefertoconceptualdatamodels
andlogicaldatabasemodelssimultaneously.Table1givessomeconceptual
datamodelsandlogicaldatabasemodelsthatmaybeappliedforengineering
informationmodeling.Thefollowingtwosub-sectionsgivethemoredetailed
explanationsaboutthesemodels.
Conceptual Data Models
Muchattentionhasbeendirectedatconceptualdatamodelingofengineering
information(Mannistoetal.,2001;McKay,Bloor,&dePennington,1996).
Product data models, for example, can be viewed as a class of semantic data
models (i.e., conceptual data models) that take into account the needs of
engineeringdata(Shaw,Bloor,&dePennington,1989).Recently,conceptual
informationmodelingofenterprisessuchasvirtualenterpriseshasreceived
increasingattention(Zhang&Li,1999).Generallyspeaking,traditionalER

(entity-relationship)andEER(extendedentity-relationship)canbeusedfor
engineeringinformationmodelingatconceptuallevel(Chen,1976).Butlimited
by their power in engineering modeling, some improved conceptual data
modelshavebeendeveloped.
IDEF1Xisamethodfordesigningrelationaldatabaseswithasyntaxdesigned
to support the semantic constructs necessary in developing a conceptual
schema.SomeresearchhasfocusedontheIDEF1Xmethodology.Athorough
treatment of the IDEF1X method can be found in Wizdom Systems Inc.
(1985).TheuseoftheIDEF1Xmethodologytobuildadatabaseformultiple
applications was addressed in Kusiak, Letsche, and Zakarian (1997).
In order to share and exchange product data, the Standard for the Exchange
of Product Model Data (STEP) is being developed by the International
OrganizationforStandardization(ISO).STEPprovidesameanstodescribe
a product model throughout its life cycle and to exchange data between
differentunits.STEPconsistsoffourmajorcategories,whicharedescription
methods, implementation methods, conformance testing methodology
and framework, and standardized application data models/schemata,
respectively.EXPRESS(Schenck&Wilson,1994),asthedescriptionmeth-
Table 1. Database models for engineering information modeling
Database Models
Conceptual Data Models Logical Database Models
Generic
Conceptual
Data Models
Specific
Conceptual
Data Models
for
Engineering
Classical
Logical
Database
Models
XML
Databases
Specific &
Hybrid
Database
Models
Extended
Database
Models
• ER data
model
• EER data
model
• UML data
model
• XML data
model
• IDEF1X
data model
• EXPRESS
data model
• Relational
databases
• Nested
relational
databases
• Object-
oriented
databases
• Object-
relational
databases
• Classical
logical
databases
• Native
XML
databases
• Active
databases
• Deductive
databases
• Constraint
databases
• Spatio-
temporal
databases
• Object-
oriented
active
databases
• Deductive
object-
relational
databases
…
• Fuzzy
relational
databases
• Fuzzy
nested
relational
databases
• Fuzzy
object-
oriented
databases
• Deductive
fuzzy
relational
databases
…

12 Ma
ods of STEP and a conceptual schema language, can model product design,
manufacturing,andproductiondata.EXPRESSmodelherebybecomesoneof
themajorconceptualdatamodelsforengineeringinformationmodeling.
WithregardtoCAD/CAMdevelopmentforproductmodeling,areviewwas
conducted in Eastman and Fereshetian (1994), and five information models
used in product modeling, namely, ER, NAIM, IDEF1X, EXPRESS and
EDM,werestudied.ComparedwithIDEF1X,EXPRESScanmodelcomplex
semanticsinengineeringapplication,includingengineeringobjectsandtheir
relationships. Based on EXPRESS model, it is easy to implement share and
exchangeengineeringinformation.
ItshouldbenotedthatER/EER,IDEF1XandEXPRESScouldmodelneither
knowledgenorfuzzyinformation.ThefirsteffortwasdoneinZvieliandChen
(1996)toextendERmodeltorepresentthreelevelsoffuzziness.Thefirstlevel
refers to the set of semantic objects, resulting in fuzzy entity sets, fuzzy
relationship sets and fuzzy attribute sets. The second level concerns the
occurrences of entities and relationships. The third level is related to the
fuzzinessinattributevaluesofentitiesandrelationships.Consequently,ER
algebra was fuzzily extended to manipulate fuzzy data. In Chen and Kerre
(1998),severalmajornotionsinEERmodelwereextended,includingfuzzy
extensiontogeneralization/specialization,andsharedsubclass/categoryaswell
asfuzzymultipleinheritance,fuzzyselectiveinheritance,andfuzzyinheritance
forderivedattributes.Morerecently,usingfuzzysetsandpossibilitydistribu-
tion (Zadeh, 1978), fuzzy extensions to IDEF1X and EXPRESS were pro-
posed in Ma, Zhang, and Ma (2002) and Ma (in press), respectively.
UML(UnifiedModelingLanguage)(Booch,Rumbaugh,&Jacobson,1998;
OMG,2003),beingstandardizedbytheObjectManagementGroup(OMG),is
asetofOOmodelingnotations.UMLprovidesacollectionofmodelstocapture
manyaspectsofasoftwaresystem.Fromtheinformationmodelingpointofview,
themostrelevantmodelistheclassmodel.Thebuildingblocksinthisclassmodel
arethoseofclassesandrelationships.TheclassmodelofUMLencompassesthe
conceptsusedinER,aswellasotherOOconcepts.Inaddition,italsopresents
theadvantageofbeingopenandextensible,allowingitsadaptationtothespecific
needsoftheapplicationsuchasworkflowmodelingofe-commerce(Changet
al., 2000) and product structure mapping (Oh, Hana, & Suhb, 2001). In
particular,theclassmodelofUMLisextendedfortherepresentationofclass
constraintsandtheintroductionofstereotypeassociations(Milietal.,2001).
WiththepopularityofWeb-baseddesign,manufacturing,andbusinessactivi-
ties, the requirement has been put on the exchange and share of engineering

informationovertheWeb.XML(eXtensibleMarkupLanguage),createdby
theWorldWideWebConsortium,letsinformationpublishersinventtheirown
tags for particular applications or work with other organizations to define
shared sets of tags that promote interoperability and that clearly separate
contentandpresentation.XMLprovidesaWeb-friendlyandwell-understood
syntaxfortheexchangeofdata.BecauseXMLimpactsondatadefinitionand
shareontheWeb(Seligman&Rosenthal,2001),XMLtechnologyhasbeen
increasingly studied, and more and more Web tools and Web servers are
capable of supporting XML. In Bourret (2004), product data markup lan-
guage, the XML for product data exchange and integration, has been devel-
oped. As to XML modeling at concept level, UML was used for designing
XML DTD (document- type definition) in Conrad, Scheffner, and Freytag
(2000). In Xiao et al. (2001), an object-oriented conceptual model was
developedtodesignXMLschema.ERmodelwasusedforconceptualdesign
of semi-structured databases in Lee et al. (2001). But XML does not support
impreciseanduncertaininformationmodelingandknowledgemodeling.Intro-
ducingimprecisionanduncertaintyintoXMLhasincreasinglybecomeatopic
ofresearch(Abiteboul,Segoufin,&Vianu,2001;Damiani,Oliboni,&Tanca,
2001; Ma, 2005).
Logical Database Models
Classical Logical Database Models
Astoengineeringinformationmodelingindatabasesystems,thegenericlogical
database models such relational databases, nested relational databases, and
object-oriented databases can be used. Also, some hybrid logical database
models such as object-relational databases are very useful for this purpose.
In Ahmed (2004), the KSS (Kraftwerk Kennzeichen System) identification
and classification system was used to develop database system for plant
maintenanceandmanagement.OntopofarelationalDBMS,anEXPRESS-
orientedinformationsystemwasbuiltinArnalteandScala(1997)forsupport-
inginformationintegrationinacomputer-integratedmanufacturingenviron-
ment. In this case, the conceptual model of the information was built in
EXPRESS and then parsed and translated to the corresponding relational
constructs.RelationaldatabasesforSTEP/EXPRESSwerealsodiscussedin
Krebs and Lührsen (1995). In addition, an object-oriented layer was devel-

14 Ma
opedinBarsalouandWiederhold(1990)tomodelcomplexentitiesontopof
arelationaldatabase.Thisdomain-independentarchitecturepermitsobject-
orientedaccesstoinformationstoredinrelationalformat-informationthatcan
besharedamongapplications.
Object-orienteddatabasesprovideanapproachforexpressingandmanipulat-
ing complex objects. A prototype object-oriented database system, called
ORION, was thus designed and implemented to support CAD (Kim et al.,
1990).Object-orienteddatabasesforSTEP/EXPRESShavebeenstudiedin
Goh et al. (1994, 1997). In addition, an object-oriented active database was
alsodesignedforSTEP/EXPRESSmodelsinDong,Y.etal.(1997).Accord-
ingtothecharacteristicsofengineeringdesign,aframeworkfortheclassifica-
tion of queries in object-oriented engineering databases was provided in
Samaras, Spooner, and Hardwick (1994), where the strategy for query
evaluation is different from traditional relational databases. Based on the
comparisonwithrelationaldatabases,theselectionsandcharacteristicsofthe
object-orienteddatabaseanddatabasemanagementsystems(OODBMS)in
manufacturing were discussed in Zhang (2001). The current studies and
applicationswerealsosummarized.
XML Databases
It is crucial for Web-based applications to model, store, manipulate, and
manage XML data documents. XML documents can be classified into data-
centricdocumentsanddocument-centricdocuments(Bourret,2004).Data-
centricdocumentsarecharacterizedbyfairlyregularstructure,fine-grained
data (i.e., the smallest independent unit of data is at the level of a PCDATA-
onlyelementoranattribute),andlittleornomixedcontent.Theorderinwhich
siblingelementsandPCDATAoccursisgenerallynotsignificant,exceptwhen
validatingthedocument.Data-centricdocumentsaredocumentsthatuseXML
as a data transport. They are designed for machine consumption and the fact
thatXMLisusedatallisusuallysuperfluous.Thatis,itisnotimportanttothe
application or the database that the data is, for some length of time, stored in
an XML document. As a general rule, the data in data-centric documents is
stored in a traditional database, such as a relational, object-oriented, or
hierarchical database. The data can also be transferred from a database to a
XML document. For the transfers between XML documents and databases,
the mapping relationships between their architectures as well as their data
shouldbecreated(Lee&Chu,2000;Surjanto,Ritter,&Loeser,2000).Note

that it is possible to discard some information such as the document and its
physicalstructurewhentransferringdatabetweenthem.Itmustbepointedout,
however,thatthedataindata-centricdocumentssuchassemi-structureddata
can also be stored in a native XML database, in which a document-centric
documentisusuallystored.Document-centricdocumentsarecharacterizedby
less regular or irregular structure, larger-grained data (that is, the smallest
independentunitofdatamightbeatthelevelofanelementwithmixedcontent
or the entire document itself), and lots of mixed content. The order in which
siblingelementsandPCDATAoccursisalmostalwayssignificant.Document-
centric documents are usually documents that are designed for human con-
sumption.Asageneralrule,thedocumentsindocument-centricdocumentsare
stored in a native XML database or a content management system (an
applicationdesignedtomanagedocumentsandbuiltontopofanativeXML
database). Native XML databases are databases designed especially for
storingXMLdocuments.TheonlydifferenceofnativeXMLdatabasesfrom
otherdatabasesisthattheirinternalmodelisbasedonXMLandnotsomething
else,suchastherelationalmodel.
In practice, however, the distinction between data-centric and document-
centricdocumentsisnotalwaysclear.Sothepreviously-mentionedrulesare
notofacertainty.Data,especiallysemi-structureddata,canbestoredinnative
XML databases, and documents can be stored in traditional databases when
fewXML-specificfeaturesareneeded.Furthermore,theboundariesbetween
traditional databases and native XML databases are beginning to blur, as
traditionaldatabasesaddnativeXMLcapabilitiesandnativeXMLdatabases
supportthestorageofdocumentfragmentsinexternaldatabases.
In Seng, Lin, Wang, and Yu (2003), a technical review of XML and XML
database technology, including storage method, mapping technique, and
transformation paradigm, was provided and an analytic and comparative
framework was developed. By collecting and compiling the IBM, Oracle,
Sybase,andMicrosoftXMLdatabaseproducts,theframeworkwasusedand
each of these XML database techniques was analyzed.
Special, Hybrid, and Extended Logical Database Models
It should be pointed out that, however, the generic logical database models
suchasrelationaldatabases,nestedrelationaldatabases,andobject-oriented
databasesdonotalwayssatisfytherequirementsofengineeringmodeling.As
pointed out in Liu (1999), relational databases do not describe the complex

16 Ma
structurerelationshipofdatanaturally,andseparaterelationsmayresultindata
inconsistencieswhenupdatingthedata.Inaddition,theproblemofinconsistent
datastillexistsinnestedrelationaldatabases,andthemechanismofsharingand
reusing CAD objects is not fully effective in object-oriented databases. In
particular,thesedatabasemodelscannothandleengineeringknowledge.Some
special databases based on relational or object-oriented models are hereby
introduced. In Dong and Goh (1998), an object-oriented active database for
engineering application was developed to support intelligent activities in
engineeringapplications.InLiu(1999),deductivedatabaseswereconsidered
asthepreferabledatabasemodelsforCADdatabases,anddeductiveobject-
relationaldatabasesforCADwereintroducedinLiuandKatragadda(2001).
Constraintdatabasesbasedonthegenericlogicaldatabasemodelsareusedto
representlargeoreveninfinitesetsinacompactwayandaresuitablehereby
for modeling spatial and temporal data (Belussi, Bertino, & Catania, 1998;
Kuper,Libkin,&Paredaens,2000).Also,itiswellestablishedthatengineering
designisaconstraint-basedactivity(Dzbor,1999;Guiffrida,&Nagi,1998;
Young,Giachetti,&Ress,1996).Soconstraintdatabasesarepromisingasa
technologyformodelingengineeringinformationthatcanbecharacterizedby
largedatainvolume,complexrelationships(structure,spatialand/ortemporal
semantics), intensive knowledge and so forth. In Posselt and Hillebrand
(2002), the issue about constraint database support for evolving data in
productdesignwasinvestigated.
It should be noted that fuzzy databases have been proposed to capture fuzzy
information in engineering (Sebastian & Antonsson, 1996; Zimmermann,
1999).Fuzzydatabasesmaybebasedonthegenericlogicaldatabasemodels
such as relational databases (Buckles & Petry, 1982; Prade & Testemale,
1984), nested relational databases (Yazici et al., 1999), and object-oriented
databases (Bordogna, Pasi, & Lucarella, 1999; George et al., 1996; van
Gyseghem & de Caluwe, 1998). Also, some special databases are extended
for fuzzy information handling. In Medina et al. (1997), the architecture for
deductive fuzzy relational database was presented, and a fuzzy deductive
object-orienteddatamodelwasproposedinBostanandYazici(1998).More
recently,howtoconstructfuzzyeventsetsautomaticallyandapplyittoactive
databaseswasinvestigatedinSayginandUlusoy(2001).

Constructions of Database Models
Dependingondataabstractlevelsandactualapplications,differentdatabase
modelshavetheiradvantagesanddisadvantages.Thisisthereasonwhythere
exist a lot of database models, conceptual ones and logical ones. It is not
appropriate to state that one database model is always better than the others.
Conceptualdatamodelsaregenerallyusedforengineeringinformationmod-
elingatahighlevelofabstraction.However,engineeringinformationsystems
are constructed based on logical database models. So at the level of data
manipulation,thatis,alowlevelofabstraction,thelogicaldatabasemodelis
usedforengineeringinformationmodeling.Here,logicaldatabasemodelsare
oftencreatedthroughmappingconceptualdatamodelsintologicaldatabase
models. This conversion is called conceptual design of databases. The
relationships among conceptual data models, logical database models, and
engineeringinformationsystemsareshowninFigure2.
In this figure,LogicalDBModel(A) andLogicalDBModel(B) are different
database systems. That means that they may have different logical database
models,sayrelationaldatabaseandobject-orienteddatabase,ortheymaybe
different database products, say Oracle™ and DB2, although they have the
same logical database model. It can be seen from the figure that a developed
conceptualdatamodelcanbemappedintodifferentlogicaldatabasemodels.
Besides,itcanalsobeseenthatalogicaldatabasemodelcanbemappedinto
a conceptual data model. This conversion is called database reverse engi-
neering.Itisclearthatitispossiblethatdifferentlogicaldatabasemodelscan
beconvertedoneanotherthroughdatabasereverseengineering.
Figure 2. Relationships among conceptual data model, logical database
model, and engineering information systems
Engineering
Information
Systems
Logical DB Model (A)
Logical DB Model (B)
Conceptual
Data
Model
Users
Users
Internet
Users
Intranet

18 Ma
Development of Conceptual Data Models
Ithasbeenshownthatdatabasemodelingofengineeringinformationgenerally
starts from conceptual data models, and then the developed conceptual data
modelsaremappedintologicaldatabasemodels.Firstofall,letusfocusonthe
choice, design, conversion, and extension of conceptual data models in
databasemodelingofengineeringinformation.
Generally speaking, ER and IDEF1X data models are good candidates for
businessprocessinengineeringapplications.Butfordesignandmanufacturing,
object-orientedconceptualdatamodelssuchEER,UML,andEXPRESSare
powerful. Being the description methods of STEP and a conceptual schema
language,EXPRESSisextensivelyacceptedinindustrialapplications.How-
ever,EXPRESSisnotagraphicalschemalanguage,unlikeEERandUML.In
order to construct EXPRESS data model at a higher level of abstract,
EXPRESS-G,beingthegraphicalrepresentationofEXPRESS,isintroduced.
Note that EXPRESS-G can only express a subset of the full language of
EXPRESS. EXPESS-G provides supports for the notions of entity, type,
relationship,cardinality,andschema.Thefunctions,procedures,andrulesin
EXPRESS language are not supported by EXPRESS-G. So EER and UML
should be used to design EXPRESS data model conceptually, and then such
EER and UML data models can be translated into EXPRESS data model.
It should be pointed out that, however, for Web-based engineering applica-
tions,XMLshouldbeusedforconceptualdatamodeling.JustlikeEXPRESS,
XML is not a graphical schema language, either. EER and UML can be used
to design XML data model conceptually, and then such EER and UML data
models can be translated into XML data model.
Thatmultiplegraphicaldatamodelscanbeemployedfacilitatesthedesigners
withdifferentbackgroundtodesigntheirconceptualmodelseasilybyusingone
ofthegraphicaldatamodelswithwhichtheyarefamiliar.However,acomplex
conceptualdatamodelisgenerallycompletedcooperativelybyadesigngroup,
in which each member may use a different graphical data model. All these
graphicaldatamodels,designedbydifferentmembers,shouldbeconverted
into a union data model finally. Furthermore, the EXPRESS schema can be
turnedintoXMLDTD.Sofar,thedatamodelconversionsamongEXPRESS-
G, IDEF1X, ER/EER, and UML only receive few attentions although such
conversionsarecrucialinengineeringinformationmodeling.In(Cherfi,Akoka,
and Comyn-Wattiau, 2002), the conceptual modeling quality between EER
and UML was investigated. In Arnold and Podehl (1999), a mapping from

EXPRESS-G to UML was introduced in order to define a linking bridge and
bringthebestoftheworldsofproductdatatechnologyandsoftwareengineer-
ing together. Also, the formal transformation of EER and EXPRESS-G was
developedinMaetal.(2003).Inaddition,thecomparisonofUMLandIDEF
was given in Noran (2000).
Figure3showsthedesignandconversionrelationshipsamongconceptualdata
models.
Inordertomodelfuzzyengineeringinformationinaconceptualdatamodel,it
is necessary to extend its modeling capability. As we know, most database
modelsmakeuseofthreelevelsofabstraction,namely,thedatadictionary,the
databaseschema,andthedatabasecontents(Erens,McKay,&Bloor,1994).
The fuzzy extensions of conceptual data models should be conducted at all
threelevelsofabstraction.Ofcourse,theconstructsofconceptualdatamodels
shouldaccordinglybeextendedtosupportfuzzyinformationmodelingatthese
threelevelsofabstraction.InZvieliandChen(1996),forexample,threelevels
of fuzziness were captured in the extended ER model. The first level is
concernedwiththeschemaandreferstothesetofsemanticobjects,resulting
infuzzyentitysets,fuzzyrelationshipsetsandfuzzyattributesets.Thesecond
levelisconcernedwiththeschema/instanceandreferstothesetofinstances,
resultinginfuzzyoccurrencesofentitiesandrelationships.Thethirdlevelis
concerned with the content and refers to the set of values, resulting in fuzzy
attributevaluesofentitiesandrelationships.
EXPRESSpermitsnullvaluesinarraydatatypesandrolenamesbyutilizingthe
keywordOptionalandusedthree-valuedlogic(False,Unknown,and True).
Inaddition,theselectdatatypeinEXPRESSdefinesonekindofimpreciseand
uncertain data type which actual type is unknown at present. So EXPRESS
indeedsupportsimpreciseinformationmodelingbutveryweakly.Furtherfuzzy
extensiontoEXPRESSisneeded.JustlikefuzzyER,fuzzyEXPRESSshould
Figure 3. Relationships among conceptual data models
EXPRESS
XML
ER/EER UML IDEF1X EXPRESS-G
Conversion Design

20 Ma
capturethreelevelsoffuzzinessanditsconstructssuchasthebasicelements
(reservedwordsandliterals),thedatatypes,theentities,theexpressionsand
so on, should hereby be extended.
Development of Logical Database Models
It should be noticed that there might be semantic incompatibility between
conceptual data models and logical database models. So when a conceptual
data model is mapped into a logical database model, we should adopt such a
logicaldatabasemodelwhichexpressivepowerisclosetotheconceptualdata
model so that the original information and semantics in the conceptual data
modelcanbepreservedandsupportedfurthest.Table2showshowrelational
and object-oriented databases fair against various conceptual data models.
Here, CDM and LDBM denote conceptual data model and logical database
model,respectively.
ItisclearfromthetablethatrelationaldatabasessupportERandIDEF1Xwell.
So, when an ER or IDEF1X data model is converted, relational databases
shouldbeused.Ofcourse,thetargetrelationaldatabasesshouldbefuzzyones
ifERorIDEF1Xdatamodelisafuzzyone.ItisalsoseenthatEER,UML,or
EXPRESS data model should be mapped into object-oriented databases.
EXPRESS is extensively accepted in industrial application area. EER and
UML, being graphical conceptual data models, can be used to design EX-
PRESSdatamodelconceptually,andthenEERandUMLdatamodelscanbe
translatedintoEXPRESSdatamodel(Oh,Hana,&Suhb,2001).Inaddition,
the EXPRESS schema can be turned into XML DTD (Burkett, 2001). So, in
thefollowing,wefocusonlogicaldatabaseimplementationofEXPRESSdata
model.
InordertoconstructalogicaldatabasearoundanEXPRESSdatamodel,the
followingtasksmustbeperformed:(1)definingthedatabasestructuresfrom
EXPRESSdatamodeland(2)providingSDAI(STEPStandardDataAccess
Table 2. Match of logical database models to conceptual data models
LDBM
CDM
Relational Databases Object-Oriented Databases
ER good bad
IDEF1X good bad
EER fair good
UML fair good
EXPRESS fair good

Interface) access to the database. Users define their databases using EX-
PRESS,manipulatethedatabasesusingSDAI,andexchangedatawithother
applicationsthroughthedatabasesystems.
Relational and Object-Oriented Database Support
for EXPRESS Data Model
In EXPRESS data models, entity instances are identified by their unique
identifiers.Entityinstancescanberepresentedastuplesinrelationaldatabases,
wherethetuplesareidentifiedbytheirkeys.Tomanipulatethedataofentity
instancesinrelationaldatabases,theproblemthatentityinstancesareidentified
inrelationaldatabasesmustberesolved.Asweknow,inEXPRESS,thereare
attributes with UNIQUE constraints. When an entity type is mapped into a
relation and each entity instance is mapped into a tuple, it is clear that such
attributes can be viewed as the key of the tuples to identify instances. So an
EXPRESS data model must contain such an attribute with UNIQUE con-
straints at least when relational databases are used to model EXPRESS data
model. In addition, inverse clause and where clause can be implemented in
relationaldatabasesastheconstraintsofforeignkeyanddomain,respectively.
Complex entities and subtype/superclass in EXPRESS data models can be
implementedinrelationaldatabasesviathereferencerelationshipsbetween
relations.Suchorganizations,however,donotnaturallyrepresentthestructural
relationships among the objects described. When users make a query, some
joinoperationsmustbeused.Therefore,object-orienteddatabasesshouldbe
used for the EXPRESS data model.
Unlike the relational databases, there is no widely accepted definition as to
whatconstitutesanobject-orienteddatabase,althoughobject-orienteddata-
base standards have been released by ODMG (2000). Not only is it true that
notallfeaturesinoneobject-orienteddatabasecanbefoundinanother,butthe
interpretation of similar features may also differ. But some features are in
commonwithobject-orienteddatabases,includingobjectidentity,complex
objects,encapsulation,types,andinheritance.EXPRESSisobject-orientedin
nature,whichsupportsthesecommonfeaturesinobject-orienteddatabases.
Therefore, there should be a more direct way to mapping EXPRESS data
model into object-oriented databases. It should be noted that there is incom-
patibilitybetweentheEXPRESSdatamodelandobject-orienteddatabases.
Nowidelyaccepteddefinitionofobject-orienteddatabasemodelresultsinthe
factthatthereisnotacommonsetofincompatibilitiesbetweenEXPRESSand

22 Ma
object-oriented databases. Some possible incompatibilities can be found in
Goh et al. (1997).
Now let us focus on fuzzy relational and object-oriented databases. As
mentioned previously, the fuzzy EXPRESS should capture three levels of
fuzziness:theschemalevel,theschema/instance,andthecontent.Depending
onthemodelingcapability,however,fuzzyrelationaldatabasesonlysupport
thelasttwolevelsoffuzziness,namely,theschema/instanceandthecontent.It
is possible that object-oriented databases are extended to support all three
levelsoffuzzinessinfuzzyEXPRESS.
Requirements and Implementation of SDAI Functions
ThegoalofSDAIistoprovidetheuserswithuniformmanipulationinterfaces
and reduce the cost of integrated product databases. When EXPRESS data
modelsaremappedintodatabases,userswillfacedatabases.Asadataaccess
interface,SDAIfallsintothecategoryoftheapplicationuserswhoaccessand
manipulatethedata.SotherequirementsofSDAIfunctionsaredecidedbythe
requirementsoftheapplicationusersofdatabases.However,SDAIitselfisin
astateofevolution.Consideringtheenormityofthetaskandthedifficultyfor
achievingagreementastowhatfunctionsaretobeincludedandtheviabilityof
implementing the suggestions, only some basic requirements such as data
query, data update, structure query, and validation are catered for. Further-
more,underfuzzyinformationenvironment,therequirementsofSDAIfunc-
tionsneededformanipulatingthefuzzyEXPRESSdatamodelmustconsider
thefuzzyinformationprocessingsuchasflexibledataquery.
Using SDAI operations, the SDAI applications can access EXPRESS data
model.However,onlythespecificationsofSDAIoperationsaregiveninSTEP
Part23andPart24.Theimplementationoftheseoperationsisempty,which
shouldbedevelopedutilizingthespecialbindinglanguageaccordingtodata-
basesystems.OnewillmeettwodifficultieswhenimplementingSDAIinthe
databases. First, the SDAI specifications are still in a state of evolution.
Second,theimplementationofSDAIfunctionsisproduct-related.Inaddition,
object-oriented databases are not standardized. It is extremely true for the
databaseimplementationoftheSDAIfunctionsneededformanipulatingthe
fuzzyEXPRESSdatamodel,becausetherearenocommercialfuzzyrelational
database management systems, and little research is done on fuzzy object-
oriented databases so far.

Itshouldbepointedoutthat,however,thereexistsahigher-levelimplementa-
tionofEXPRESSdatamodelthandatabaseimplementation,whichisknowl-
edge-based. Knowledge-based implementation has the features of database
implementations, plus full support for EXPRESS constraint validation. A
knowledge-basedsystemshouldreadandwriteexchangefiles,makeproduct
dataavailabletoapplicationsinstructuresdefinedbyEXPRESS,workondata
stored in a central database, and should be able to reason about the contents
of the database. Knowledge-based systems encode rules using techniques
suchasframes,semanticnets,andvariouslogicsystems,andthenuseinference
techniques such as forward and backward chaining to reason about the
contentsofadatabase.Althoughsomeinterestingpreliminaryworkwasdone,
knowledge-based implementations do not exist. Deductive databases and
constraint databases based on relational and/or object-oriented database
models are useful in knowledge-intensive engineering applications for this
purpose.Indeductivedatabases,rulescanbemodeledandknowledgebases
areherebyconstituted.Inconstraintdatabases,complexspatialand/ortempo-
raldatacanbemodeled.Inparticular,constraintdatabasescanhandleawealth
ofconstraintsinengineeringdesign.
Conclusion
Manufacturingenterprisesobtainincreasingproductvarietiesandproducts
with lower price, high quality and shorter lead time by using enterprise
information systems. The enterprise information systems have become the
nervecenterofcurrentcomputer-basedmanufacturingenterprises.Manufac-
turingengineeringistypicallyadata-andknowledge-intensiveapplicationarea
and engineering information modeling is hereby one of the crucial tasks to
implementengineeringinformationsystems.Databasesaredesignedtosupport
datastorage,processing,andretrievalactivitiesrelatedtodatamanagement,
and database systems are the key to implementing engineering information
modeling. But the current mainstream databases are mainly designed for
businessapplications.Therearesomeuniquerequirementsfromengineering
informationmodeling,whichimposeachallengetodatabasestechnologiesand
promotetheirevolvement.Itisespeciallytrueforcontemporaryengineering
applications,wheresomenewtechniqueshavebeenincreasinglyappliedand
their operational patterns are hereby evolved (e.g., e-manufacturing, Web-

24 Ma
based PDM, etc.). One can find many researches in literature that focus on
using database techniques for engineering information modeling to support
variousengineeringactivities.Itshouldbenotedthat,however,mostofthese
papersonlydiscusssomeoftheissuesaccordingtothedifferentviewpointsand
applicationrequirements.Engineeringinformationmodelingiscomplexbe-
cause it should cover product life cycle times. On the other hand, databases
coverwidevarietyoftopicsandevolvequickly.Currently,fewpapersprovide
comprehensivediscussionsabouthowcurrentengineeringinformationmodel-
ingcanbesupportedbydatabasetechnologies.Thischaptertriestofillthisgap.
Inthischapter,wefirstidentifysomerequirementsforengineeringinformation
modeling,whichincludecomplexobjectsandrelationships,dataexchangeand
share,Web-basedapplications,imprecisionanduncertainty,andknowledge
management.Sincethecurrentmainstreamdatabasesaremainlydesignedfor
businessapplications,andthedatabasemodelscanbeclassifiedintoconceptual
data models and logical database models, we then investigate how current
conceptualdatamodelsandlogicaldatabasemodelssatisfytherequirementsof
engineeringinformationmodelingindatabases.Thepurposeofengineering
informationmodelingindatabasesistoconstructthelogicaldatabasemodels,
whicharethefoundationoftheengineeringinformationsystems.Generallythe
constructionsoflogicaldatabasemodelsstartfromtheconstructionsofconcep-
tualdatamodelsandthenthedevelopedconceptualdatamodelsareconverted
intothelogicaldatabasemodels.Sothechapterpresentsnotonlythedevelop-
mentofsomeconceptualdatamodelsforengineeringinformationmodeling,but
alsothedevelopmentoftherelationalandobject-orienteddatabaseswhichare
usedtoimplementEXPRESS/STEP.Thecontributionofthechapteristoidentify
thedirectionofdatabasestudyviewedfromengineeringapplicationsandprovide
aguidanceofinformationmodelingforengineeringdesign,manufacturing,and
productionmanagement.Itcanbebelievedthatsomemorepowerfuldatabase
modelswillbedevelopedtosatisfyengineeringinformationmodeling.
References
Abiteboul,S.,Segoufin,L.,&Vianu,V.(2001).Representingandquerying
XML with incomplete information, In Proceedings of the 12th ACM
SIGACT-SIGMOD-SIGART Symposium on Principles of Database
Systems, California (pp. 150-161).

Ahmed,S.(2004).Classificationstandardinlargeprocessplantsforintegra-
tion with robust database. Industrial Management & Data Systems,
104(8), 667-673.
Antonsoon, E. K., & Otto, K. N. (1995). Imprecision in engineering design.
ASME Journal of Mechanical Design, 117(B), 25-32.
Arnalte, S., & Scala, R. M. (1997). An information system for computer-
integratedmanufacturingsystems. RoboticsandComputer-Integrated
Manufacturing, 13(3), 217-228.
Arnold, F., & Podehl, G. (1999). Best of both worlds — A mapping from
EXPRESS-G to UML. Lecture Notes in Computer Science, Vol. 1618,
49-63.
Barsalou, T., & Wiederhold, G. (1990). Complex objects for relational
databases. Computer-Aided Design, 22(8), 458-468.
Belussi, A., Bertino, E., & Catania, B. (1998). An extended algebra for
constraint databases. IEEE Transactions on Knowledge and Data
Engineering, 10(5), 686-705.
Ben-Arieh,D.,Chopra,M.,&Bleyberg,M.Z.(1998).Dataminingapplica-
tionforreal-timedistributedshopfloorcontrol.InProceedingsof1998
IEEE International Conference on Systems, Man, and Cybernetics,
San Diego, California (pp. 2738-2743).
Booch, G., Rumbaugh, J., & Jacobson, I. (1998). The unified modeling
languageuserguide.Reading,MA:Addison-WelsleyLongman.
Bordogna,G.,Pasi,G.,&Lucarella,D.(1999).Afuzzyobject-orienteddata
model for managing vague and uncertain information. International
Journal of Intelligent Systems, 14, 623-651.
Bostan, B., & Yazici, A. (1998). A fuzzy deductive object-oriented data
model. In Proceedings of the IEEE International Conference on
Fuzzy Systems, Alaska (vol. 2, pp. 1361-1366). IEEE.
Bourret, R. (2004). XML and databases. Retrieved October 2004, from
http://www.rpbourret.com/xml/XMLAndDatabases.htm
Buckles, B. P., & Petry, F. E. (1982). A fuzzy representation of data for
relational database. Fuzzy Sets and Systems, 7(3), 213-226.
Burkett, W. C. (2001). Product data markup language: A new paradigm for
productdataexchangeandintegration.ComputerAidedDesign,33(7),
489-500.

26 Ma
Caldwell, N. H. M., Clarkson, Rodgers, & Huxor (2000). Web-based
knowledge management for distributed design. IEEE Intelligent Sys-
tems, 15(3), 40-47.
Caputo,M.(1996).Uncertainty,flexibilityandbuffersinthemanagementof
the firm operating system. Production Planning & Control, 7(5), 518-
528.
Chang,Y.L.,etal.(2000).Workflowprocessdefinitionandtheirapplications
in e-commerce. In Proceedings of International Symposium on Mul-
timediaSoftwareEngineering,Taiwan(pp.193-200).IEEEComputer
Society.
Chen, G. Q., & Kerre, E. E. (1998). Extending ER/EER concepts towards
fuzzy conceptual data modeling. In Proceedings of the 1998 IEEE
International Conference on Fuzzy Systems, Alaska (vol. 2, pp. 1320-
1325). IEEE.
Chen,P.P.(1976).Theentity-relationshipmodel:Towardaunifiedviewof
data. ACM Transactions on Database Systems, 1(1), 9-36.
Cherfi, S. S. S., Akoka, J., & Comyn-Wattiau, I. (2002). Conceptual
modeling quality — From EER to UML schemas evaluation. Lecture
Notes in Computer Science, Vol. 250, 414-428.
Chiadamrong,N.(1999).Anintegratedfuzzymulti-criteriadecision-making
methodformanufacturingstrategyselection.ComputersandIndustrial
Engineering, 37, 433-436.
Chu, X. J., & Fan, Y. Q. (1999). Product data management based on Web
technology. Integrated Manufacturing Systems, 10(2), 84-88.
CIMdata. (1997). Product data management: The definition. Retrieved
September 1997,fromhttp://www.cimdata.com
Conrad, R., Scheffner, D., & Freytag, J. C. (2000). XML conceptual
modeling using UML. Lecture Notes in Computer Science, Vol. 1920,
558-571.
Damiani,E.,Oliboni,B.,&Tanca,L.(2001).FuzzytechniquesforXMLdata
smushing. Lecture Notes in Computer Science, Vol. 2206, 637-652.
deSouza,R.,Ying,Z.Z.,&Yang,L.C.(1998).Modelingbusinessprocesses
and enterprise activities at the knowledge level. Artificial Intelligence
for Engineering Design, Analysis and Manufacturing: AIEDAM,
12(1), 29-42.
Dong,Y.,&Goh,A.(1998).Anintelligentdatabaseforengineeringapplica-
tions. Artificial Intelligence in Engineering, 12, 1-14.

Dong,Y.,etal.(1997).ActivedatabasesupportforSTEP/EXPRESSmodels.
Journal of Intelligent Manufacturing, 8(4), 251-261.
Dubois, D., Fargier, H., & Prade, H. (1995). Fuzzy constraints in job-shop
scheduling. Journal of Intelligent Manufacturing, 6(4), 215-234.
Dubois, D., Fargier, H., & Prade, H. (1996). Possibility theory in constraint
satisfaction problems: Handling priority, preference, and uncertainty.
Applied Intelligence, 6, 287-309.
Dubois, D., & Prade, H. (1996). What are fuzzy rules and how to use them.
Fuzzy Sets and Systems, 84, 169-185.
Dzbor,M.(1999).Intelligentsupportforproblemformalizationindesign.In
Proceedings of the 3rd IEEE Conference on Intelligent Engineering
Systems, Stara Lesna, Slovakia (pp. 279-284). IEEE.
Eastman, C. M., & Fereshetian, N. (1994). Information models for use in
product design: A comparison. Computer-Aide Design, 26(7), 551-
572.
Erens, F., McKay, A., & Bloor, S. (1994). Product modeling using multiple
levelsofabstract:Instancesastypes. ComputersinIndustry,24,17-28.
Fargier, H., & Thierry, C. (1999). The use of qualitative decision theory in
manufacturingplanningandcontrol:Recentresultsinfuzzymasterpro-
duction scheduling. In R. Slowinski, & M. Hapke (Eds.), Advances in
scheduling and sequencing under fuzziness (pp. 45-59). Heidelberg:
Physica-Verlag.
Fensel, D., Ding, & Omelayenko (2001). Product data integration in B2B e-
commerce. IEEE Intelligent Systems and Their Applications, 16(4),
54-59.
Francois,F.,&Bigeon,J.(1995).IntegrationoffuzzytechniquesinaCAD-
CAM system. IEEE Transactions on Magnetics, 31(3), 1996-1999.
George, R., et al. (1996). Uncertainty management issues in the object-
oriented data model. IEEE Transactions on Fuzzy Systems, 4(2), 179-
192.
Gertosio,C.,&Dussauchoy,A.(2004).Knowledgediscoveryformindustrial
databases. Journal of Intelligent Manufacturing, 15, 29-37.
Giachetti,R.E.,Young,Roggatz,Eversheim,&Perrone(1997).Amethod-
ologyforthereductionofimprecisionintheengineeringdesignprocess.
European Journal of Operations Research, 100(2), 277-292.

28 Ma
Goh, A., et al. (1994). A study of SDAI implementation on object-oriented
databases. Computer Standards & Interfaces, 16, 33-43.
Goh, A., et al. (1997). A STEP/EXPRESS to object-oriented databases
translator. International Journal of Computer Applications in Tech-
nology, 10(1-2), 90-96.
Grabot,B.,&Geneste,L.(1998).Managementofimprecisionanduncertainty
forproductionactivitycontrol.JournalofIntelligentManufacturing,9,
431-446.
Guiffrida,A.,&Nagi,R.(1998).Fuzzysettheoryapplicationsinproduction
managementresearch:Aliteraturesurvey.JournalofIntelligentManu-
facturing, 9, 39-56.
Ho, C. F., Wu, W. H., & Tai, Y. M. (2004). Strategies for the adaptation of
ERP systems. Industrial Management & Data Systems, 104(3), 234-
251.
Issa, G., Shen, S., & Chew, M. S. (1994). Using analogical reasoning for
mechanism design. IEEE Expert, 9(3), 60-69.
Johtela, T., Smed, Johnsson, & Nevalainen (1999) A fuzzy approach for
modelingmultiplecriteriainthejobgroupingproblem.InProceedingsof
the 25th International Conference on Computers & Industrial Engi-
neering, New Orleans, LA (pp. 447-50).
Jones, J. D., & Hua, Y. (1998). A fuzzy knowledge base to support routine
engineering design. Fuzzy Sets and Systems, 98, 267-278.
Kapadia,R.,&Fromherz,M.P.J.(1997).Designoptimizationwithuncertain
application knowledge. In Proceedings of the 10th International
Conference on Industrial and Engineering Application of Artificial
Intelligence and Expert Systems, Atlanta, GA (pp. 421-430). Gordon
andBreachSciencePublishers.
Karwowski, W., & Evans, G. W. (1986). Fuzzy concepts in production
management research: A review. International Journal of Production
Research, 24(1), 129-147.
Kim, K., Cormier, O’Grady, & Young (1995). A system for design and
concurrentengineeringunderimprecision.JournalofIntelligentManu-
facturing, 6(1), 11-27.
Kim, W., et al. (1990). Object-oriented database support for CAD. Com-
puter-Aided Design, 22(8), 521-550.

Kleissner, C. (1998). Data mining for the enterprise. In Proceedings of the
Thirty-First Annual Hawaii International Conference on System
Sciences, Hawaii (vol. 7, pp. 295-304). IEEE Computer Society.
Kosala,R.,&Blockeel,H.(2000).Webminingresearch:Asurvey.SIGKDD
Explorations, 2(1), 1-15.
Krebs,T.,&Lührsen,H.(1995).STEPdatabasesasintegrationplatformfor
concurrent engineering. In Proceedings of the 2nd International Con-
ferenceonConcurrentEngineering,Virginia(pp.131-142).Johnstown,
PA:ConcurrentTechnologies.
Kuper, G., Libkin, L., & Paredaens, J. (2000). Constraint databases.
SpringerVerlag.
Kusiak,A.,Letsche,T.,&Zakarian,A.(1997).DatamodelingwithIDEF1X.
International Journal of Computer Integrated Manufacturing, 10(6),
470-486.
Kusiak,A.,&Tseng,T.L.(2000).Datamininginengineeringdesign:Acase
study. In Proceedings of the IEEE Conference on Robotics and
Automation, San Francisco (pp. 206-211). IEEE.
Lee, D. W., & Chu, W. W. (2000). Constraints-preserving transformation
fromXMLdocumenttypedefinitiontorelationalschema.LectureNotes
in Computer Science, Utah (vol. 1920, pp. 323-338).
Lee,M.L.,etal.(2001).Designingsemi-structureddatabases:Aconceptual
approach. Lecture Notes in Computer Science, Vol. 2113 (pp. 12-21).
Liu, D. T., & Xu, X. W. (2001). A review of Web-based product data
management systems. Computers in Industry, 44(3), 251-262.
Liu, M. C. (1999). On CAD databases. In Proceedings of the 1999 IEEE
Canadian Conference on Electrical and Computer Engineering,
Edmonton, Canada (pp. 325-330). IEEE Computer Society.
Liu, M. C., & Katragadda, S. (2001). DrawCAD: Using deductive object-
relational databases in CAD. Lecture Notes in Artificial Intelligence
(vol. 2113, pp. 481-490). Munich, Germany: Springer.
Ma, Z. M. (2005). Fuzzy database modeling with XML. Springer.
Ma, Z. M. (in press). Extending EXPRESS for imprecise and uncertain
engineeringinformationmodeling.JournalofIntelligentManufactur-
ing.
Ma,Z.M.,&Mili,F.(2003).Knowledgecomparisonindesignrepositories.
Engineering Applications of Artificial Intelligence, 16(3), 203-211.

30 Ma
Ma,Z.M.,etal.(2003).Conceptualdatamodelsforengineeringinformation
modelingandformaltransformationofEERandEXPRESS-G.Lecture
Notes in Computer Science, Vol. 2813(pp. 573-575). Springer Verlag.
Ma, Z. M., Zhang, W. J., & Ma, W. Y. (2002). Extending IDEF1X to model
fuzzy data. Journal of Intelligent Manufacturing, 13(4), 295-307.
Maedche, A., Motik, Stojanovic, Studer, & Volz (2003). Ontologies for
enterpriseknowledgemanagement.IEEEIntelligentSystems,18(2),2-
9.
Mannisto, T., Peltonen, Soininen, & Sulonen (2001). Multiple abstraction
levels in modeling product structures. Date and Knowledge Engineer-
ing, 36(1), 55-78.
Manwaring,M.L.,Jones,K.L.,&Glagowski,T.G.(1996).Anengineering
designprocesssupportedbyknowledgeretrievalfromaspatialdatabase.
In Proceedings of Second IEEE International Conference on Engi-
neering of Complex Computer Systems, Montreal, Canada (pp. 395-
398). IEEE Computer Society.
McKay, A., Bloor, M. S., & de Pennington, A. (1996). A framework for
product data. IEEE Transactions on Knowledge and Data Engineer-
ing, 8(5), 825-837.
Medina,J.M.,etal.(1997).FREDDI:Afuzzyrelationaldeductivedatabase
interface. International Journal of Intelligent Systems, 12(8), 597-
613.
Michael,S.M.,&Khemani,D.(2002).Knowledgemanagementinmanufac-
turingtechnology:AnA.I.applicationintheindustry.InProceedingsof
the 2002 International Conference on Enterprise Information Sys-
tems, Ciudad Real, Spain (pp. 506-511).
Mili,F.,Shen,Martinez,Noel,Ram,&Zouras(2001).Knowledgemodeling
for design decisions. Artificial Intelligence in Engineering, 15, 153-
164.
Mitra, S., Pal, S. K., & Mitra, P. (2002). Data mining in soft computing
framework: A survey. IEEE Transactions on Neural Networks, 13(1),
3-14.
Muller, K., & Sebastian, H. J. (1997). Intelligent systems for engineering
design and configuration problems. European Journal of Operational
Research, 100, 315-326.

Discovering Diverse Content Through
Random Scribd Documents

V
Vambrace, Construction of, 6
Van der Goes, Picture in Glasgow by, 50
Vaulting Master, The, 113
Verney Memoirs, mention of proof of armour, 68
— — — — fit of armour, 105
Versy, 12
Vervelles, 46
Vienna, Armour in, 14, 133-41, 143, 145
— Brigandine in, 50
— Helm-cap in, 89
— Helmet-covers in, 93
Vireton, 64
W
Wallace helm, 18, 117
— Collection, Horse-armour in, 9
— — Armour in, 134, 139, 145
— — Bascinet and camail in, 46
— — Tools in, 24
Waller, J. G., his views on banded mail, 48
Walsingham, 49
Way, Albert, 107
Weisz Künig, 15, 141, 142
— — Armourer’s tools figured in, 28
Westminster helm, 17, 18, 119
— Workshops in, 32
Whalebone used for gloves and jacks, 100
Whetstone, his project for light armour of proof, 59
Willars de Honnecourt, 45
William the Conqueror, 1
Willoughby, Jack of Sir John, 49
Windsor Park Tournament, 29, 100
Wire-drawing, Invention of, 44

Woolvercote, Sword-mills at, 34
Woolwich Rotunda, Tools in the, 24
— — helm, 18
— — leather guns, 102
Z
Zeller, Walter, 92
Zurich, 18

Printed by
William Brendon and Son, Ltd.
Plymouth

TRANSCRIBER’S NOTE
Footnotes [10] to [18] have multiple anchors on page 25.
Footnote [80] has two anchors on page 63.
Footnote [129] has two anchors on page 119.
Footnote [138] has three anchors on page 127.
The Frontispiece, Plates II, XV and XXI were sideways in the original book, and
have been rotated to display horizontally.
For consistency with all other extracts from old documents, the extract on page
107 is displayed in a smaller font.
Obvious typographical errors and punctuation errors have been corrected after
careful comparison with other occurrences within the text and consultation of
external sources.
Except for those changes noted below, all misspellings in the text, and
inconsistent or archaic usage, have been retained.
Pg xiii: page number ‘vii’ replaced by ‘ix’.
Pg 20: ‘often exhibition some’ replaced by ‘often exhibiting some’.
Pg 26: ‘but the “hurthestaff”’ replaced by ‘but the “hurthestaf”’.
Pg 26: ‘The “cottyngyr” and’ replaced by ‘The “cottyngyre” and’.
Pg 40: ‘Gay’s Encylopædia’ replaced by ‘Gay’s Encyclopædia’.
Pg 87: ‘seur ledii jacques’ replaced by ‘seur ledit jacques’.
Fig. 48 caption: ‘Ashmolean Musem’ replaced by ‘Ashmolean Museum’.
Pg 111: ‘26 genouillère’ replaced by ‘26 genouillière’.
Pg 129: ‘Grünewald, Hans’ replaced by ‘Grünewalt, Hans’.
Fig. 66: is displayed on the right hand side of the page, to avoid overlaying the
sidenote on handheld devices. (It was displayed on the left hand side just under
the sidenote in the original book.)
Pg 151: ‘Hans Guïnewalt’ replaced by ‘Hans Grünewalt’.
Pg 173: ‘blank space’ replaced by ‘ ... ’.
Pg 174: ‘blank space’ replaced by ‘ ... ’.
GLOSSARY.
Section ‘O’: ‘Oberarmzeng’ replaced by ‘Oberarmzeug’.
Entries for ‘javelin’ ‘bravette’ ‘lists’ are referenced but they do not exist.

INDEX.
There were several references to the Preface at pages ‘vii’ and ‘viii’. This
numbering was incorrect and has been changed to ‘ix’ and ‘x’.
Kelk: ‘“Manakine,” 125’ replaced by ‘“Mannakine,” 125’.
La Noue: ‘armour, 116’ replaced by ‘armour, 117’.

*** END OF THE PROJECT GUTENBERG EBOOK THE ARMOURER
AND HIS CRAFT FROM THE XITH TO THE XVITH CENTURY ***
Updated editions will replace the previous one—the old editions
will be renamed.
Creating the works from print editions not protected by U.S.
copyright law means that no one owns a United States
copyright in these works, so the Foundation (and you!) can copy
and distribute it in the United States without permission and
without paying copyright royalties. Special rules, set forth in the
General Terms of Use part of this license, apply to copying and
distributing Project Gutenberg™ electronic works to protect the
PROJECT GUTENBERG™ concept and trademark. Project
Gutenberg is a registered trademark, and may not be used if
you charge for an eBook, except by following the terms of the
trademark license, including paying royalties for use of the
Project Gutenberg trademark. If you do not charge anything for
copies of this eBook, complying with the trademark license is
very easy. You may use this eBook for nearly any purpose such
as creation of derivative works, reports, performances and
research. Project Gutenberg eBooks may be modified and
printed and given away—you may do practically ANYTHING in
the United States with eBooks not protected by U.S. copyright
law. Redistribution is subject to the trademark license, especially
commercial redistribution.
START: FULL LICENSE

THE FULL PROJECT GUTENBERG LICENSE

PLEASE READ THIS BEFORE YOU DISTRIBUTE OR USE THIS WORK
To protect the Project Gutenberg™ mission of promoting the
free distribution of electronic works, by using or distributing this
work (or any other work associated in any way with the phrase
“Project Gutenberg”), you agree to comply with all the terms of
the Full Project Gutenberg™ License available with this file or
online at www.gutenberg.org/license.
Section 1. General Terms of Use and
Redistributing Project Gutenberg™
electronic works
1.A. By reading or using any part of this Project Gutenberg™
electronic work, you indicate that you have read, understand,
agree to and accept all the terms of this license and intellectual
property (trademark/copyright) agreement. If you do not agree
to abide by all the terms of this agreement, you must cease
using and return or destroy all copies of Project Gutenberg™
electronic works in your possession. If you paid a fee for
obtaining a copy of or access to a Project Gutenberg™
electronic work and you do not agree to be bound by the terms
of this agreement, you may obtain a refund from the person or
entity to whom you paid the fee as set forth in paragraph 1.E.8.
1.B. “Project Gutenberg” is a registered trademark. It may only
be used on or associated in any way with an electronic work by
people who agree to be bound by the terms of this agreement.
There are a few things that you can do with most Project
Gutenberg™ electronic works even without complying with the
full terms of this agreement. See paragraph 1.C below. There
are a lot of things you can do with Project Gutenberg™
electronic works if you follow the terms of this agreement and
help preserve free future access to Project Gutenberg™
electronic works. See paragraph 1.E below.

1.C. The Project Gutenberg Literary Archive Foundation (“the
Foundation” or PGLAF), owns a compilation copyright in the
collection of Project Gutenberg™ electronic works. Nearly all the
individual works in the collection are in the public domain in the
United States. If an individual work is unprotected by copyright
law in the United States and you are located in the United
States, we do not claim a right to prevent you from copying,
distributing, performing, displaying or creating derivative works
based on the work as long as all references to Project
Gutenberg are removed. Of course, we hope that you will
support the Project Gutenberg™ mission of promoting free
access to electronic works by freely sharing Project Gutenberg™
works in compliance with the terms of this agreement for
keeping the Project Gutenberg™ name associated with the
work. You can easily comply with the terms of this agreement
by keeping this work in the same format with its attached full
Project Gutenberg™ License when you share it without charge
with others.
1.D. The copyright laws of the place where you are located also
govern what you can do with this work. Copyright laws in most
countries are in a constant state of change. If you are outside
the United States, check the laws of your country in addition to
the terms of this agreement before downloading, copying,
displaying, performing, distributing or creating derivative works
based on this work or any other Project Gutenberg™ work. The
Foundation makes no representations concerning the copyright
status of any work in any country other than the United States.
1.E. Unless you have removed all references to Project
Gutenberg:
1.E.1. The following sentence, with active links to, or other
immediate access to, the full Project Gutenberg™ License must
appear prominently whenever any copy of a Project
Gutenberg™ work (any work on which the phrase “Project

Gutenberg” appears, or with which the phrase “Project
Gutenberg” is associated) is accessed, displayed, performed,
viewed, copied or distributed:
This eBook is for the use of anyone anywhere in the United
States and most other parts of the world at no cost and
with almost no restrictions whatsoever. You may copy it,
give it away or re-use it under the terms of the Project
Gutenberg License included with this eBook or online at
www.gutenberg.org. If you are not located in the United
States, you will have to check the laws of the country
where you are located before using this eBook.
1.E.2. If an individual Project Gutenberg™ electronic work is
derived from texts not protected by U.S. copyright law (does not
contain a notice indicating that it is posted with permission of
the copyright holder), the work can be copied and distributed to
anyone in the United States without paying any fees or charges.
If you are redistributing or providing access to a work with the
phrase “Project Gutenberg” associated with or appearing on the
work, you must comply either with the requirements of
paragraphs 1.E.1 through 1.E.7 or obtain permission for the use
of the work and the Project Gutenberg™ trademark as set forth
in paragraphs 1.E.8 or 1.E.9.
1.E.3. If an individual Project Gutenberg™ electronic work is
posted with the permission of the copyright holder, your use and
distribution must comply with both paragraphs 1.E.1 through
1.E.7 and any additional terms imposed by the copyright holder.
Additional terms will be linked to the Project Gutenberg™
License for all works posted with the permission of the copyright
holder found at the beginning of this work.
1.E.4. Do not unlink or detach or remove the full Project
Gutenberg™ License terms from this work, or any files

containing a part of this work or any other work associated with
Project Gutenberg™.
1.E.5. Do not copy, display, perform, distribute or redistribute
this electronic work, or any part of this electronic work, without
prominently displaying the sentence set forth in paragraph 1.E.1
with active links or immediate access to the full terms of the
Project Gutenberg™ License.
1.E.6. You may convert to and distribute this work in any binary,
compressed, marked up, nonproprietary or proprietary form,
including any word processing or hypertext form. However, if
you provide access to or distribute copies of a Project
Gutenberg™ work in a format other than “Plain Vanilla ASCII” or
other format used in the official version posted on the official
Project Gutenberg™ website (www.gutenberg.org), you must,
at no additional cost, fee or expense to the user, provide a copy,
a means of exporting a copy, or a means of obtaining a copy
upon request, of the work in its original “Plain Vanilla ASCII” or
other form. Any alternate format must include the full Project
Gutenberg™ License as specified in paragraph 1.E.1.
1.E.7. Do not charge a fee for access to, viewing, displaying,
performing, copying or distributing any Project Gutenberg™
works unless you comply with paragraph 1.E.8 or 1.E.9.
1.E.8. You may charge a reasonable fee for copies of or
providing access to or distributing Project Gutenberg™
electronic works provided that:
• You pay a royalty fee of 20% of the gross profits you derive
from the use of Project Gutenberg™ works calculated using the
method you already use to calculate your applicable taxes. The
fee is owed to the owner of the Project Gutenberg™ trademark,
but he has agreed to donate royalties under this paragraph to
the Project Gutenberg Literary Archive Foundation. Royalty

payments must be paid within 60 days following each date on
which you prepare (or are legally required to prepare) your
periodic tax returns. Royalty payments should be clearly marked
as such and sent to the Project Gutenberg Literary Archive
Foundation at the address specified in Section 4, “Information
about donations to the Project Gutenberg Literary Archive
Foundation.”
• You provide a full refund of any money paid by a user who
notifies you in writing (or by e-mail) within 30 days of receipt
that s/he does not agree to the terms of the full Project
Gutenberg™ License. You must require such a user to return or
destroy all copies of the works possessed in a physical medium
and discontinue all use of and all access to other copies of
Project Gutenberg™ works.
• You provide, in accordance with paragraph 1.F.3, a full refund of
any money paid for a work or a replacement copy, if a defect in
the electronic work is discovered and reported to you within 90
days of receipt of the work.
• You comply with all other terms of this agreement for free
distribution of Project Gutenberg™ works.
1.E.9. If you wish to charge a fee or distribute a Project
Gutenberg™ electronic work or group of works on different
terms than are set forth in this agreement, you must obtain
permission in writing from the Project Gutenberg Literary
Archive Foundation, the manager of the Project Gutenberg™
trademark. Contact the Foundation as set forth in Section 3
below.
1.F.
1.F.1. Project Gutenberg volunteers and employees expend
considerable effort to identify, do copyright research on,
transcribe and proofread works not protected by U.S. copyright

law in creating the Project Gutenberg™ collection. Despite these
efforts, Project Gutenberg™ electronic works, and the medium
on which they may be stored, may contain “Defects,” such as,
but not limited to, incomplete, inaccurate or corrupt data,
transcription errors, a copyright or other intellectual property
infringement, a defective or damaged disk or other medium, a
computer virus, or computer codes that damage or cannot be
read by your equipment.
1.F.2. LIMITED WARRANTY, DISCLAIMER OF DAMAGES - Except
for the “Right of Replacement or Refund” described in
paragraph 1.F.3, the Project Gutenberg Literary Archive
Foundation, the owner of the Project Gutenberg™ trademark,
and any other party distributing a Project Gutenberg™ electronic
work under this agreement, disclaim all liability to you for
damages, costs and expenses, including legal fees. YOU AGREE
THAT YOU HAVE NO REMEDIES FOR NEGLIGENCE, STRICT
LIABILITY, BREACH OF WARRANTY OR BREACH OF CONTRACT
EXCEPT THOSE PROVIDED IN PARAGRAPH 1.F.3. YOU AGREE
THAT THE FOUNDATION, THE TRADEMARK OWNER, AND ANY
DISTRIBUTOR UNDER THIS AGREEMENT WILL NOT BE LIABLE
TO YOU FOR ACTUAL, DIRECT, INDIRECT, CONSEQUENTIAL,
PUNITIVE OR INCIDENTAL DAMAGES EVEN IF YOU GIVE
NOTICE OF THE POSSIBILITY OF SUCH DAMAGE.
1.F.3. LIMITED RIGHT OF REPLACEMENT OR REFUND - If you
discover a defect in this electronic work within 90 days of
receiving it, you can receive a refund of the money (if any) you
paid for it by sending a written explanation to the person you
received the work from. If you received the work on a physical
medium, you must return the medium with your written
explanation. The person or entity that provided you with the
defective work may elect to provide a replacement copy in lieu
of a refund. If you received the work electronically, the person
or entity providing it to you may choose to give you a second
opportunity to receive the work electronically in lieu of a refund.

If the second copy is also defective, you may demand a refund
in writing without further opportunities to fix the problem.
1.F.4. Except for the limited right of replacement or refund set
forth in paragraph 1.F.3, this work is provided to you ‘AS-IS’,
WITH NO OTHER WARRANTIES OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
MERCHANTABILITY OR FITNESS FOR ANY PURPOSE.
1.F.5. Some states do not allow disclaimers of certain implied
warranties or the exclusion or limitation of certain types of
damages. If any disclaimer or limitation set forth in this
agreement violates the law of the state applicable to this
agreement, the agreement shall be interpreted to make the
maximum disclaimer or limitation permitted by the applicable
state law. The invalidity or unenforceability of any provision of
this agreement shall not void the remaining provisions.
1.F.6. INDEMNITY - You agree to indemnify and hold the
Foundation, the trademark owner, any agent or employee of the
Foundation, anyone providing copies of Project Gutenberg™
electronic works in accordance with this agreement, and any
volunteers associated with the production, promotion and
distribution of Project Gutenberg™ electronic works, harmless
from all liability, costs and expenses, including legal fees, that
arise directly or indirectly from any of the following which you
do or cause to occur: (a) distribution of this or any Project
Gutenberg™ work, (b) alteration, modification, or additions or
deletions to any Project Gutenberg™ work, and (c) any Defect
you cause.
Section 2. Information about the Mission
of Project Gutenberg™

Project Gutenberg™ is synonymous with the free distribution of
electronic works in formats readable by the widest variety of
computers including obsolete, old, middle-aged and new
computers. It exists because of the efforts of hundreds of
volunteers and donations from people in all walks of life.
Volunteers and financial support to provide volunteers with the
assistance they need are critical to reaching Project
Gutenberg™’s goals and ensuring that the Project Gutenberg™
collection will remain freely available for generations to come. In
2001, the Project Gutenberg Literary Archive Foundation was
created to provide a secure and permanent future for Project
Gutenberg™ and future generations. To learn more about the
Project Gutenberg Literary Archive Foundation and how your
efforts and donations can help, see Sections 3 and 4 and the
Foundation information page at www.gutenberg.org.
Section 3. Information about the Project
Gutenberg Literary Archive Foundation
The Project Gutenberg Literary Archive Foundation is a non-
profit 501(c)(3) educational corporation organized under the
laws of the state of Mississippi and granted tax exempt status
by the Internal Revenue Service. The Foundation’s EIN or
federal tax identification number is 64-6221541. Contributions
to the Project Gutenberg Literary Archive Foundation are tax
deductible to the full extent permitted by U.S. federal laws and
your state’s laws.
The Foundation’s business office is located at 809 North 1500
West, Salt Lake City, UT 84116, (801) 596-1887. Email contact
links and up to date contact information can be found at the
Foundation’s website and official page at
www.gutenberg.org/contact

Section 4. Information about Donations to
the Project Gutenberg Literary Archive
Foundation
Project Gutenberg™ depends upon and cannot survive without
widespread public support and donations to carry out its mission
of increasing the number of public domain and licensed works
that can be freely distributed in machine-readable form
accessible by the widest array of equipment including outdated
equipment. Many small donations ($1 to $5,000) are particularly
important to maintaining tax exempt status with the IRS.
The Foundation is committed to complying with the laws
regulating charities and charitable donations in all 50 states of
the United States. Compliance requirements are not uniform
and it takes a considerable effort, much paperwork and many
fees to meet and keep up with these requirements. We do not
solicit donations in locations where we have not received written
confirmation of compliance. To SEND DONATIONS or determine
the status of compliance for any particular state visit
www.gutenberg.org/donate.
While we cannot and do not solicit contributions from states
where we have not met the solicitation requirements, we know
of no prohibition against accepting unsolicited donations from
donors in such states who approach us with offers to donate.
International donations are gratefully accepted, but we cannot
make any statements concerning tax treatment of donations
received from outside the United States. U.S. laws alone swamp
our small staff.
Please check the Project Gutenberg web pages for current
donation methods and addresses. Donations are accepted in a
number of other ways including checks, online payments and

credit card donations. To donate, please visit:
www.gutenberg.org/donate.
Section 5. General Information About
Project Gutenberg™ electronic works
Professor Michael S. Hart was the originator of the Project
Gutenberg™ concept of a library of electronic works that could
be freely shared with anyone. For forty years, he produced and
distributed Project Gutenberg™ eBooks with only a loose
network of volunteer support.
Project Gutenberg™ eBooks are often created from several
printed editions, all of which are confirmed as not protected by
copyright in the U.S. unless a copyright notice is included. Thus,
we do not necessarily keep eBooks in compliance with any
particular paper edition.
Most people start at our website which has the main PG search
facility: www.gutenberg.org.
This website includes information about Project Gutenberg™,
including how to make donations to the Project Gutenberg
Literary Archive Foundation, how to help produce our new
eBooks, and how to subscribe to our email newsletter to hear
about new eBooks.

Welcome to Our Bookstore - The Ultimate Destination for Book Lovers
Are you passionate about books and eager to explore new worlds of
knowledge? At our website, we offer a vast collection of books that
cater to every interest and age group. From classic literature to
specialized publications, self-help books, and children’s stories, we
have it all! Each book is a gateway to new adventures, helping you
expand your knowledge and nourish your soul
Experience Convenient and Enjoyable Book Shopping Our website is more
than just an online bookstore—it’s a bridge connecting readers to the
timeless values of culture and wisdom. With a sleek and user-friendly
interface and a smart search system, you can find your favorite books
quickly and easily. Enjoy special promotions, fast home delivery, and
a seamless shopping experience that saves you time and enhances your
love for reading.
Let us accompany you on the journey of exploring knowledge and
personal growth!
ebookgate.com

Database Modeling for Industrial Data Management Emerging Technologies and Applications Zongmin Ma

More Related Content

Similar to Database Modeling for Industrial Data Management Emerging Technologies and Applications Zongmin Ma (20)

Recently uploaded (20)

Database Modeling for Industrial Data Management Emerging Technologies and Applications Zongmin Ma