Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information PDF
Preview Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information
ZongminMa FuzzyDatabaseModelingofImpreciseandUncertainEngineeringInformation StudiesinFuzzinessandSoftComputing,Volume195 Editor-in-chief Prof.JanuszKacprzyk SystemsResearchInstitute PolishAcademyofSciences ul.Newelska6 01-447Warsaw Poland E-mail:[email protected] Furthervolumesofthisseries Vol.187.ZhongLi,WolfgangA.Halang, canbefoundonourhomepage: GuanrongChen(Eds.) IntegrationofFuzzyLogicandChaos springer.com Theory,2006 ISBN3-540-26899-5 Vol.179.MirceaNegoita, Vol.188.JamesJ.Buckley,LeonardJ. BerndReusch(Eds.) Jowers RealWorldApplicationsofComputational SimulatingContinuousFuzzySystems,2006 Intelligence,2005 ISBN3-540-28455-9 ISBN3-540-25006-9 Vol.189.HansBandemer Vol.180.WesleyChu, MathematicsofUncertainty,2006 TsauYoungLin(Eds.) 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ISBN3-540-30609-9 KnowledgeMining,2005 ISBN3-540-25070-0 Vol.195.ZongminMa FuzzyDatabaseModelingofImpreciseand Vol.186.RadimBeˇlohlávek,Vilém UncertainEngineeringInformation,2006 Vychodil ISBN3-540-30675-7 FuzzyEquationalLogic,2005 ISBN3-540-26254-7 Zongmin Ma Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information ABC Dr.ZongminMa CollegeofInformationScience&Engineering NortheasternUniversity Shenyang,Liaoning110004 People’sRepublicofChina E-mail:[email protected] LibraryofCongressControlNumber:2005936458 ISSNprintedition:1434-9922 ISSNelectronicedition:1860-0808 ISBN-10 3-540-30675-7SpringerBerlinHeidelbergNewYork ISBN-13 978-3-540-30675-7SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsare liableforprosecutionundertheGermanCopyrightLaw. SpringerisapartofSpringerScience+BusinessMedia springer.com (cid:1)c Springer-VerlagBerlinHeidelberg2006 PrintedinTheNetherlands Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnotimply, evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelaws andregulationsandthereforefreeforgeneraluse. Typesetting:bytheauthorandTechBooksusingaSpringerLATEXmacropackage Printedonacid-freepaper SPIN:11353270 89/TechBooks 543210 Dedicated to My Parents and My Wife Li, My Daughter Ruizhe, and My Son Jiaji Preface Computer-based information technologies have been extensively used to help industries manage their processes and information systems hereby be- come their nervous center. More specially, databases are designed to sup- port the data storage, processing, and retrieval activities related to data management in information systems. Database management systems pro- vide efficient task support and database systems are the key to implement- ing industrial data management. Industrial data management requires data- base technique support. Industrial applications, however, are typically data and knowledge intensive applications and have some unique characteris- tics that makes their management difficult. Besides, some new techniques such as Web, artificial intelligence, and etc. have been introduced into in- dustrial applications. These unique characteristics and usage of new tech- nologies have put many potential requirements on industrial data manage- ment, which challenge today’s database systems and promote their evolvement. Viewed from database technology, information modeling in databases can be identified at two levels: (conceptual) data modeling and (logical) database modeling. This results in conceptual (semantic) data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into a chosen logical database schema. Database systems based on logical database model are used to build information systems for data manage- ment. Much attention has been directed at conceptual data modeling of in- dustrial information systems. Product data models, for example, can be views as a class of semantic data models (i.e., conceptual data models) that take into account the needs of engineering data. As it may be known, in many real-world applications, information is of- ten vague or ambiguous. Therefore, different kinds of imperfect informa- tion have extensively been introduced and studied to model the real world as accurately as possible. In addition to complex structures and rich se- mantic relationships, one also needs to model imprecise and uncertain in- formation in many industrial activities. Information imprecision and uncer- tainty exist in almost all engineering applications and has been investigated in the context of various engineering actions. Classical data- base models often suffer from their incapability of representing and VIII Preface manipulating imprecise and uncertain information. Since the early 1980’s, Zadeh’s fuzzy logic has been used to extend various database models in order to enhance the classical models such that uncertain and imprecise in- formation can be represented and manipulated. This resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. Since classical relational database model and its extension of fuzziness do not satisfy the need of modeling complex objects with imprecision and uncertainty, currently many researches have been concentrated on fuzzy conceptual data models and fuzzy object-oriented database models in order to deal with complex objects and uncertain data together. The research on fuzzy conceptual models and fuzzy object- oriented databases is receiving increasing attention in addition to fuzzy re- lational database model. It should be noticed, however, that there have been few efforts at investigating the issues of database modeling of impre- cise and uncertain industrial information although databases have been widely applied in industrial applications. The material in this book is the outgrowth of research the author has conducted in recent years. The topics include fuzzy conceptual data model- ing of industrial information and database implementations of fuzzy con- ceptual data models for industrial information. Concerning the fuzzy con- ceptual data modeling of industrial information, in addition to the ER/EER and UML data models, the IDEF1X data model and the EXPRESS data model are extended for fuzzy industrial data modeling. Concerning the da- tabase implementations of fuzzy conceptual data models for industrial in- formation, the conversion of the fuzzy conceptual data models to the fuzzy logical databases are investigated, in which the fuzzy logical databases in- clude the fuzzy relational databases, fuzzy nested relational databases and fuzzy object-oriented databases. The mappings from the fuzzy IDEF1X model to the fuzzy relational databases and from the fuzzy EXPRESS-G model to the fuzzy nested relational databases are developed in addition to the mappings from the fuzzy ER model to the fuzzy relational databases and from the EER model to the fuzzy object-oriented databases. In particu- lar, the object-oriented database implementation of the fuzzy EXPRESS model is introduced in this book. This book aims to provide a single record of current research and practi- cal applications in the fuzzy database modeling of industrial information. The objective of the book is to provide state of the art information to the researchers of industrial database modeling and while at the same time serve the information technology professional faced with a non-traditional industrial application that defeats conventional approaches. Researchers, graduate students, and information technology professionals interested in Preface IX industrial databases and soft computing will find this book a starting point and a reference for their study, research and development. I would like to acknowledge all of the researchers in the area of data- base modeling of industrial information and fuzzy databases. Based on both their publications and the many discussions with some of them, their influence on this book is profound. Much of the material presented in this book is a continuation of the initial research work that I did during my Ph.D. studies at City University of Hong Kong. I am grateful for the finan- cial support from City University of Hong Kong through a research stu- dentship. Additionally, the assistances and facilities of University of Sas- katchewan and University of Sherbrooke, Canada, Oakland University and Wayne State University, USA, and Northeastern University and City Uni- versity of Hong Kong, China, are deemed important, and are highly appre- ciated. Special thanks go to the publishing team at Springer-Verlag. In par- ticular to series editors Dr. Janusz Kacprzyk and Dr. Thomas Ditzinger and to their assistant Heather King for their advice and help to propose, prepare and publish this book. This book will not be completed without the support from them. Finally I wish to thank my family for their patience, under- standing, encouragement, and support when I needed to devote many time in development of this book. This book will not be completed without their love. China Zongmin Ma October 2005 Contents 1 Engineering Information Modeling in Databases.....................................1 1.1 Introduction......................................................................................1 1.2 Conceptual Data Models...................................................................2 1.2.1 ER/EER Models........................................................................2 1.2.2 UML Class Model.....................................................................7 1.2.3 IDEF1X Model..........................................................................9 1.2.4 EXPRESS Model.....................................................................15 1.3 Logical Database Models...............................................................19 1.3.1 Relational Database Model......................................................19 1.3.2 Nested Relational Database Model..........................................21 1.3.3 Object-Oriented Database Model............................................23 1.4 Constructions of Database Models.................................................26 1.4.1 Development of Conceptual Data Models...............................27 1.4.2 Development of Logical Database Models.............................28 1.5 Summary.........................................................................................29 References............................................................................................30 2 Information Imprecision and Uncertainty in Engineering......................33 2.1 Introduction....................................................................................33 2.2 Imprecision and Uncertainty in Engineering..................................33 2.2.1 Product Preliminary Design.....................................................34 2.2.2 R2R Controller Implementation..............................................37 2.2.3 Production Activity Control (PAC).........................................38 2.3 Representation of Imprecise and Uncertain Engineering Information...........................................................................................42 2.4 Summary.........................................................................................44 References............................................................................................44 3 Fuzzy Sets and Possibility Distributions................................................47 3.1 Introduction....................................................................................47 3.2 Imperfect Information.....................................................................47 3.2.1 Null and Partial Values............................................................48 3.2.2 Probabilistic Values.................................................................49 XII Contents 3.2.3 Fuzzy Values...........................................................................49 3.3 Representations of Fuzzy Sets and Possibility Distributions..........50 3.4 Operations on Fuzzy Sets...............................................................52 3.4.1 Set Operations.........................................................................52 3.4.2 Arithmetic Operations.............................................................53 3.4.3 Relational Operations..............................................................54 3.4.4 Logical Operations..................................................................55 3.5 Summary.........................................................................................56 References............................................................................................56 4 The Fuzzy ER/EER and UML Data Models...........................................59 4.1 Introduction....................................................................................59 4.2 The Fuzzy ER/EER Data Models...................................................60 4.2.1 Three Levels of Fuzziness in ER Data Model.........................60 4.2.2 The Fuzzy EER Data Model....................................................61 4.3 The Fuzzy UML Data Model.........................................................66 4.3.1 Fuzzy Class..............................................................................66 4.3.2 Fuzzy Generalization...............................................................68 4.3.3 Fuzzy Aggregation..................................................................71 4.3.4 Fuzzy Association...................................................................73 4.3.5 Fuzzy Dependency..................................................................76 4.4 Summary.........................................................................................76 References............................................................................................77 5 The Fuzzy IDEF1X Models....................................................................79 5.1 Introduction....................................................................................79 5.2 Fuzzy Entities and Fuzzy Entity Instances.....................................79 5.3 Fuzzy Attributes and Fuzzy Attribute Values.................................81 5.4 Fuzzy Connection Relationships....................................................83 5.5 Fuzzy Non-specific Relationships..................................................84 5.6 Fuzzy Categorization Relationships...............................................85 5.7 Summary.........................................................................................87 References............................................................................................87 6 The Fuzzy EXPRESS Model..................................................................89 6.1 Introduction....................................................................................89 6.2 Fuzziness in Basic Elements...........................................................90 6.2.1 Reserved Words.......................................................................90 6.2.2 Literals.....................................................................................90 6.3 Fuzzy Data Type Modeling with EXPRESS..................................94 6.3.1 Pseudo Types...........................................................................95 6.3.2 Simple Data Types..................................................................95
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