Data modeling essentials /:
"This book provides expert tutelage for data modelers, business analysts, and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later ch...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston :
Morgan Kaufmann Publishers,
©2005.
©2005 |
| Ausgabe: | 3rd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This book provides expert tutelage for data modelers, business analysts, and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters delve into advanced subjects, including business rules, data warehousing, and enterprise-wide modeling and data management."--Jacket. |
| Beschreibung: | 1 online resource (xxvii, 532 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780080488677 0080488676 1592782515 9781592782512 1417561564 9781417561568 9780126445510 0126445516 |
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| 100 | 1 | |a Simsion, Graeme C. |1 https://id.oclc.org/worldcat/entity/E39PBJrm8F4w3y3HKpPh7KH3cP |0 http://id.loc.gov/authorities/names/n93096585 | |
| 245 | 1 | 0 | |a Data modeling essentials / |c Graeme C. Simsion and Graham C. Witt. |
| 250 | |a 3rd ed. | ||
| 260 | |a Amsterdam ; |a Boston : |b Morgan Kaufmann Publishers, |c ©2005. | ||
| 264 | 4 | |c ©2005 | |
| 300 | |a 1 online resource (xxvii, 532 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Part I. The basics : 1. What is data modeling? : A data-centered perspective -- A simple example -- Design, choice, and creativity -- Why is the data model important? : Leverage ; Conciseness ; Data quality -- What makes a good data model? : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Elegance ; Communication ; Integration ; Conflicting objectives -- Performance -- Database design stages and deliverables : Conceptual, logical, and physical data models ; The three-schema architecture and terminology -- Where do data models fit in? : Process-driven approaches ; Data driven approaches ; Parallel (blended) approaches ; Object-oriented approaches ; Prototyping approaches ; Agile methods -- Who should be involved in data modeling? -- Is data modeling still relevant? : Costs and benefits of data modeling ; Data modeling and packaged software ; Data integration ; Data warehouses ; Personal computing and user-developed systems ; Data modeling and XML -- Alternative approaches to data modeling -- Terminology -- 2. Basics of sound structure : An informal example of normalization -- Relational notation -- A more complex example -- Determining columns : One fact per column ; Hidden data ; Derivable data ; Determining the primary key -- Repeating groups and first normal form : Limit on maximum number of occurrences ; Data reusability and program complexity ; Recognizing repeating groups ; Removing repeating groups ; Determining the primary key of the new table ; First normal form -- Second and third normal forms : Problems with tables in first normal form ; Eliminating redundancy ; Determinants ; Third normal form -- Definitions and a few refinements : Determinants and functional dependency ; Primary keys ; Candidate keys ; A more formal definition of third normal form ; Foreign keys ; Referential integrity ; Update anomalies ; Denormalization and unnormalization ; Column and table names -- Choice, creativity, and normalization -- Terminology -- 3. The entity-relationship approach : A diagrammatic representation : The basic symbols: boxes and arrows ; Diagrammatic representation of foreign keys ; Interpreting the diagram ; Optionality ; Verifying the model ; Redundant arrows -- The top-down approach: entity-relationship modeling : Developing the diagram top down ; Terminology -- Entity classes : Entity diagramming convention ; Entity class naming ; Entity class definitions -- Relationships : Relationship diagramming conventions ; Many-to-many relationships ; One-to-one relationships ; Self-referencing relationships ; Relationships involving three or more entity classes ; Transferability ; Dependent and independent entity classes ; Relationship names -- Attributes : Attribute identification and definition ; Primary keys and the conceptual model -- Myths and folklore : Entity classes without relationships ; Allowed combinations of cardinality and optionality -- Creativity and E-R modeling -- 4. Subtypes and supertypes : Different levels of generalization -- Rules versus stability -- Using subtypes and supertypes as entity classes : Naming subtypes -- Diagramming conventions : Boxes in boxes ; UML conventions ; Using tools that do not support subtyping -- Definitions -- Attributes of supertypes and subtypes -- Nonoverlapping and exhaustive -- Overlapping subtypes and roles : Ignoring real-world overlaps ; Modeling only the supertypes ; Modeling the roles as participation in relationships ; Using role entity classes and one-to-one relationships ; Multiple partitions -- Hierarchy of subtypes -- Benefits of using subtypes and supertypes : Creativity ; Presentation: level of detail ; Communication ; Input to the design of views ; Classifying common patterns ; Divide and conquer -- When do we stop supertyping and subtyping? : Differences in identifiers ; Different attribute groups ; Different relationships ; Different processes ; Migration from one subtype to another ; Communication ; Capturing meaning and rules ; A pragmatic approach -- Generalization of relationships : Generalizing several one-to-many relationships to a single many-to-many relationship ; Generalizing several one-to-many relationships to a single one-to-many relationships ; Generalizing one-to-many and many-to-many relationships -- Theoretical background -- 5. Attributes and columns : Attribute definition ; Attribute disaggregation: One fact per attribute : Simple aggregation ; Conflated codes ; Meaningful ranges ; Inappropriate generalization -- Types of attributes : DBMS datatypes ; The attribute taxonomy in detail ; Attribute domains ; Column datatype and length requirements ; Conversion between external and internal representations -- Attribute names : Objectives of standardizing attribute names ; Some guidelines for attribute naming -- Attribute generalization : Options and trade-offs -- Attribute generalization resulting from entity generalization ; Attribute generalization with entity classes ; "First among equals" ; Limits to attribute generalizations -- 6. Primary keys and identity : Basic requirements and trade-offs -- Basic technical criteria : Applicability ; Uniqueness ; Minimality ; Stability -- Surrogate keys : Performance and programming issues ; Matching real-world identifiers ; Should surrogate keys be visible? ; Subtypes and surrogate keys -- Structured keys : When to use structured keys ; Programming and structured keys ; Running out of numbers -- Multiple candidate keys : Choosing a primary key ; Normalization issues -- Guidelines for choosing keys : Tables implementing independent entity classes ; Tables implementing dependent entity classes and many-to-many relationships -- Partially-null keys -- 7. Extensions and alternatives : Extensions to the basic E-R approach : Advanced attribute concepts -- The Chen E-R approach : The basic conventions ; Relationships with attributes ; Relationships involving three or more entity classes ; Roles ; The weak entity concept ; Chen conventions in practice -- Using UML object class diagrams : A conceptual data model in UML ; Advantages of UML -- Object role modeling. | |
| 505 | 0 | |a Part II: Putting it all together : 8. Organizing the data modeling task : Data modeling in the real world -- Key issues in project organization -- Key issues in project organization : Recognition of data modeling ; Clear use of the data model ; Access to users and other business stakeholders ; Conceptual, logical, and physical models ; Cross-checking with the process model ; Appropriate tools -- Roles and responsibilities -- Partitioning large projects -- Maintaining the model : Examples of complex changes ; Managing change in the modeling process -- Packaging it up -- 9. The business requirements : Purpose of the requirements phase -- The business case -- Interviews and workshops : Should you model in interviews and workshops? ; Interviews with senior managers ; Interviews with subject matter experts ; Facilitated workshops -- Riding the trucks -- Existing systems and reverse engineering -- Process models -- Object class hierarchies : Classifying object classes ; A typical set of top-level object classes -- Developing an object class hierarchy -- Potential issues ; Advantages of the object class hierarchy technique -- 10. Conceptual data modeling : Designing real models -- Learning from designers in other disciplines -- Starting the modeling -- Pattern and generic models : Using patterns ; Using a generic model ; Adapting generic models from other applications ; Developing a generic model ; When there is not a generic model -- Bottom-up modeling -- Top-down modeling -- When the problem is too complex -- Hierarchies, networks, and chains : Hierarchies Networks (many-to-many relationships) ; Chains (one-to-one relationships) -- One-to-one relationships : Distinct real-world concepts ; Separating attribute groups ; Transferable one-to-one relationships ; Self-referencing one-to-one relationships ; Support for creativity -- Developing entity class definitions -- Handling exceptions -- The right attitude : Being aware ; Being creative ; Analyzing or designing ; Being brave ; Being understanding and understood -- Evaluating the model -- Direct review of data model diagrams -- Comparison with the process model ; Testing the model with sample data -- Prototypes -- The assertion approach : Naming conventions ; Rules for generating assertions -- 11. Logical database design : Overview of the transformations required -- Table specification : The standard transformation ; Exclusion of entity classes from the database ; Classification entity classes ; Many-to-many relationship implementation ; Relationships involving more than two entity classes ; Supertype/subtype implementation -- Basic column definition : Attribute implementation: the standard transformation ; Category attribute implementation ; Derivable attributes ; Attributes of relationships ; Complex attributes ; Multivalued attribute implementation ; Additional columns ; Column datatypes ; Column nullability -- Primary key specification -- Foreign key specification : One-to-many relationship implementation ; One-to-one relationship implementation ; Derivable relationships ; Optional relationships ; Overlapping foreign keys ; Split foreign keys -- Table and column names -- Logical data model notations -- 12. Physical database design : Inputs to database design -- Options available to the database designer -- Design decisions which do not affect program logic : Indexes ; Data storage ; Memory usage -- Crafting queries to run faster : Locking -- Logical schema decisions : Alternative implementation of relationships ; Table splitting ; Table merging ; Duplication ; Denormalization ; Ranges ; Hierarchies ; Integer storage of dates and times ; Additional tables -- Views : Views of supertypes and subtypes ; Inclusion of derived attributes in views ; Denormalization and views ; Views of split and merged tables. | |
| 505 | 0 | |a Part III: Advanced topics : 13. Advanced normalization : Introduction to the higher normal forms : Common misconceptions -- Boyce-Codd normal form : Example of structure in 3NF but not in BCNF ; Definition of BCNF ; Enforcement of rules versus BCNF ; A note on domain key normal form -- Fourth normal form (4NF) and Fifth normal form (5NF) : Data in BCHF but not in 4NF ; Fifth normal form (5NF) ; Recognizing 4NF and 5NF situations ; Checking for 4NF and 5FN with the business specialist -- Beyond 5NF: splitting tables based on candidate keys -- Other normalization issues : Normalization and redundancy ; Reference tables produced by normalization ; Selecting the primary key after removing repeating groups ; Sequence of normalization and cross-table anomalies -- Advanced normalization in perspective -- 14. Modeling business rules : Types of business rules : Data rules ; Process rules ; What rules are relevant to the data modeler? -- Discovery and verification of business rules : Cardinality rules ; Other data validation rules ; Data derivation rules -- Documentation of business rules : Documentation in an E-R diagram ; Documenting other rules ; Use of subtypes to document rules -- Implementing business rules : Where to implement particular rules ; Implementation options: a detailed example ; Implementing mandatory relationships ; Referential integrity ; Restricting an attribute to a discrete set of values ; Rules involving multiple attributes ; Recording data that supports rules ; Rules that may be broken ; Enforcement of rules through primary key selection -- Rules of recursive relationships : Types of rules on recursive relationships ; Documenting rules on recursive relationships ; Implementing constraints on recursive relationships ; Analogous rules in many-to-many relationships -- 15. Time-dependent data : The problem -- When do we add the time dimension? -- Audit trails and snapshots : The basic audit trail approach ; Handling nonnumeric data ; The basic snapshot approach -- Sequences and versions -- Handling deletions -- Archiving -- Modeling time-dependent relationships : One-to-many relationships ; Many-to-many relationships ; Self-referencing relationships -- Date tables -- Temporal business rules -- Changes to the data structure -- Putting it into practice -- 16. Modeling for data warehouses and data marts : Characteristics of data warehouses and data marts : Data integration: working with existing databases ; Loads rather than updates ; Less predictable database "hits" ; Complex queries: Simple interface ; History ; Summarization -- Quality criteria for warehouse and mart models : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Simplicity and elegance ; Communication effectiveness ; Performance -- The basic design principle -- Modeling for the data warehouse : An initial model ; Understanding existing data ; Determining requirements ; Determining sources and dealing with differences ; Shaping data of data marts -- Modeling for the data mart : The basic challenge ; Multidimensional databases, stars and snowflakes ; Modeling time-dependent data -- 17. Enterprise data models and data management : : Data management : Problems of data management ; Managing data as a shared resource ; The evolution of data management -- Classification of existing data -- A target for planning -- A context for specifying new databases : Determining scope and interfaces ; Incorporating the enterprise data model in the development life cycle -- Guidance for database design -- Input to business planning -- Specification of an enterprise database -- Characteristics of enterprise data models -- Developing an enterprise data model : The development cycle ; Partitioning the task ; Inputs to the task ; Expertise requirements ; External standards -- Choice, creativity, and enterprise data models. | |
| 520 | 1 | |a "This book provides expert tutelage for data modelers, business analysts, and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters delve into advanced subjects, including business rules, data warehousing, and enterprise-wide modeling and data management."--Jacket. | |
| 650 | 0 | |a Database design. |0 http://id.loc.gov/authorities/subjects/sh88004472 | |
| 650 | 0 | |a Data structures (Computer science) |0 http://id.loc.gov/authorities/subjects/sh85035862 | |
| 650 | 6 | |a Bases de données |x Conception. | |
| 650 | 6 | |a Structures de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Desktop Applications |x Databases. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Database Management |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x System Administration |x Storage & Retrieval. |2 bisacsh | |
| 650 | 7 | |a Data structures (Computer science) |2 fast | |
| 650 | 7 | |a Database design |2 fast | |
| 650 | 7 | |a Datenbankentwurf |2 gnd |0 http://d-nb.info/gnd/4127613-9 | |
| 650 | 7 | |a Datenmodell |2 gnd |0 http://d-nb.info/gnd/4192516-6 | |
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| contents | Part I. The basics : 1. What is data modeling? : A data-centered perspective -- A simple example -- Design, choice, and creativity -- Why is the data model important? : Leverage ; Conciseness ; Data quality -- What makes a good data model? : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Elegance ; Communication ; Integration ; Conflicting objectives -- Performance -- Database design stages and deliverables : Conceptual, logical, and physical data models ; The three-schema architecture and terminology -- Where do data models fit in? : Process-driven approaches ; Data driven approaches ; Parallel (blended) approaches ; Object-oriented approaches ; Prototyping approaches ; Agile methods -- Who should be involved in data modeling? -- Is data modeling still relevant? : Costs and benefits of data modeling ; Data modeling and packaged software ; Data integration ; Data warehouses ; Personal computing and user-developed systems ; Data modeling and XML -- Alternative approaches to data modeling -- Terminology -- 2. Basics of sound structure : An informal example of normalization -- Relational notation -- A more complex example -- Determining columns : One fact per column ; Hidden data ; Derivable data ; Determining the primary key -- Repeating groups and first normal form : Limit on maximum number of occurrences ; Data reusability and program complexity ; Recognizing repeating groups ; Removing repeating groups ; Determining the primary key of the new table ; First normal form -- Second and third normal forms : Problems with tables in first normal form ; Eliminating redundancy ; Determinants ; Third normal form -- Definitions and a few refinements : Determinants and functional dependency ; Primary keys ; Candidate keys ; A more formal definition of third normal form ; Foreign keys ; Referential integrity ; Update anomalies ; Denormalization and unnormalization ; Column and table names -- Choice, creativity, and normalization -- Terminology -- 3. The entity-relationship approach : A diagrammatic representation : The basic symbols: boxes and arrows ; Diagrammatic representation of foreign keys ; Interpreting the diagram ; Optionality ; Verifying the model ; Redundant arrows -- The top-down approach: entity-relationship modeling : Developing the diagram top down ; Terminology -- Entity classes : Entity diagramming convention ; Entity class naming ; Entity class definitions -- Relationships : Relationship diagramming conventions ; Many-to-many relationships ; One-to-one relationships ; Self-referencing relationships ; Relationships involving three or more entity classes ; Transferability ; Dependent and independent entity classes ; Relationship names -- Attributes : Attribute identification and definition ; Primary keys and the conceptual model -- Myths and folklore : Entity classes without relationships ; Allowed combinations of cardinality and optionality -- Creativity and E-R modeling -- 4. Subtypes and supertypes : Different levels of generalization -- Rules versus stability -- Using subtypes and supertypes as entity classes : Naming subtypes -- Diagramming conventions : Boxes in boxes ; UML conventions ; Using tools that do not support subtyping -- Definitions -- Attributes of supertypes and subtypes -- Nonoverlapping and exhaustive -- Overlapping subtypes and roles : Ignoring real-world overlaps ; Modeling only the supertypes ; Modeling the roles as participation in relationships ; Using role entity classes and one-to-one relationships ; Multiple partitions -- Hierarchy of subtypes -- Benefits of using subtypes and supertypes : Creativity ; Presentation: level of detail ; Communication ; Input to the design of views ; Classifying common patterns ; Divide and conquer -- When do we stop supertyping and subtyping? : Differences in identifiers ; Different attribute groups ; Different relationships ; Different processes ; Migration from one subtype to another ; Communication ; Capturing meaning and rules ; A pragmatic approach -- Generalization of relationships : Generalizing several one-to-many relationships to a single many-to-many relationship ; Generalizing several one-to-many relationships to a single one-to-many relationships ; Generalizing one-to-many and many-to-many relationships -- Theoretical background -- 5. Attributes and columns : Attribute definition ; Attribute disaggregation: One fact per attribute : Simple aggregation ; Conflated codes ; Meaningful ranges ; Inappropriate generalization -- Types of attributes : DBMS datatypes ; The attribute taxonomy in detail ; Attribute domains ; Column datatype and length requirements ; Conversion between external and internal representations -- Attribute names : Objectives of standardizing attribute names ; Some guidelines for attribute naming -- Attribute generalization : Options and trade-offs -- Attribute generalization resulting from entity generalization ; Attribute generalization with entity classes ; "First among equals" ; Limits to attribute generalizations -- 6. Primary keys and identity : Basic requirements and trade-offs -- Basic technical criteria : Applicability ; Uniqueness ; Minimality ; Stability -- Surrogate keys : Performance and programming issues ; Matching real-world identifiers ; Should surrogate keys be visible? ; Subtypes and surrogate keys -- Structured keys : When to use structured keys ; Programming and structured keys ; Running out of numbers -- Multiple candidate keys : Choosing a primary key ; Normalization issues -- Guidelines for choosing keys : Tables implementing independent entity classes ; Tables implementing dependent entity classes and many-to-many relationships -- Partially-null keys -- 7. Extensions and alternatives : Extensions to the basic E-R approach : Advanced attribute concepts -- The Chen E-R approach : The basic conventions ; Relationships with attributes ; Relationships involving three or more entity classes ; Roles ; The weak entity concept ; Chen conventions in practice -- Using UML object class diagrams : A conceptual data model in UML ; Advantages of UML -- Object role modeling. Part II: Putting it all together : 8. Organizing the data modeling task : Data modeling in the real world -- Key issues in project organization -- Key issues in project organization : Recognition of data modeling ; Clear use of the data model ; Access to users and other business stakeholders ; Conceptual, logical, and physical models ; Cross-checking with the process model ; Appropriate tools -- Roles and responsibilities -- Partitioning large projects -- Maintaining the model : Examples of complex changes ; Managing change in the modeling process -- Packaging it up -- 9. The business requirements : Purpose of the requirements phase -- The business case -- Interviews and workshops : Should you model in interviews and workshops? ; Interviews with senior managers ; Interviews with subject matter experts ; Facilitated workshops -- Riding the trucks -- Existing systems and reverse engineering -- Process models -- Object class hierarchies : Classifying object classes ; A typical set of top-level object classes -- Developing an object class hierarchy -- Potential issues ; Advantages of the object class hierarchy technique -- 10. Conceptual data modeling : Designing real models -- Learning from designers in other disciplines -- Starting the modeling -- Pattern and generic models : Using patterns ; Using a generic model ; Adapting generic models from other applications ; Developing a generic model ; When there is not a generic model -- Bottom-up modeling -- Top-down modeling -- When the problem is too complex -- Hierarchies, networks, and chains : Hierarchies Networks (many-to-many relationships) ; Chains (one-to-one relationships) -- One-to-one relationships : Distinct real-world concepts ; Separating attribute groups ; Transferable one-to-one relationships ; Self-referencing one-to-one relationships ; Support for creativity -- Developing entity class definitions -- Handling exceptions -- The right attitude : Being aware ; Being creative ; Analyzing or designing ; Being brave ; Being understanding and understood -- Evaluating the model -- Direct review of data model diagrams -- Comparison with the process model ; Testing the model with sample data -- Prototypes -- The assertion approach : Naming conventions ; Rules for generating assertions -- 11. Logical database design : Overview of the transformations required -- Table specification : The standard transformation ; Exclusion of entity classes from the database ; Classification entity classes ; Many-to-many relationship implementation ; Relationships involving more than two entity classes ; Supertype/subtype implementation -- Basic column definition : Attribute implementation: the standard transformation ; Category attribute implementation ; Derivable attributes ; Attributes of relationships ; Complex attributes ; Multivalued attribute implementation ; Additional columns ; Column datatypes ; Column nullability -- Primary key specification -- Foreign key specification : One-to-many relationship implementation ; One-to-one relationship implementation ; Derivable relationships ; Optional relationships ; Overlapping foreign keys ; Split foreign keys -- Table and column names -- Logical data model notations -- 12. Physical database design : Inputs to database design -- Options available to the database designer -- Design decisions which do not affect program logic : Indexes ; Data storage ; Memory usage -- Crafting queries to run faster : Locking -- Logical schema decisions : Alternative implementation of relationships ; Table splitting ; Table merging ; Duplication ; Denormalization ; Ranges ; Hierarchies ; Integer storage of dates and times ; Additional tables -- Views : Views of supertypes and subtypes ; Inclusion of derived attributes in views ; Denormalization and views ; Views of split and merged tables. Part III: Advanced topics : 13. Advanced normalization : Introduction to the higher normal forms : Common misconceptions -- Boyce-Codd normal form : Example of structure in 3NF but not in BCNF ; Definition of BCNF ; Enforcement of rules versus BCNF ; A note on domain key normal form -- Fourth normal form (4NF) and Fifth normal form (5NF) : Data in BCHF but not in 4NF ; Fifth normal form (5NF) ; Recognizing 4NF and 5NF situations ; Checking for 4NF and 5FN with the business specialist -- Beyond 5NF: splitting tables based on candidate keys -- Other normalization issues : Normalization and redundancy ; Reference tables produced by normalization ; Selecting the primary key after removing repeating groups ; Sequence of normalization and cross-table anomalies -- Advanced normalization in perspective -- 14. Modeling business rules : Types of business rules : Data rules ; Process rules ; What rules are relevant to the data modeler? -- Discovery and verification of business rules : Cardinality rules ; Other data validation rules ; Data derivation rules -- Documentation of business rules : Documentation in an E-R diagram ; Documenting other rules ; Use of subtypes to document rules -- Implementing business rules : Where to implement particular rules ; Implementation options: a detailed example ; Implementing mandatory relationships ; Referential integrity ; Restricting an attribute to a discrete set of values ; Rules involving multiple attributes ; Recording data that supports rules ; Rules that may be broken ; Enforcement of rules through primary key selection -- Rules of recursive relationships : Types of rules on recursive relationships ; Documenting rules on recursive relationships ; Implementing constraints on recursive relationships ; Analogous rules in many-to-many relationships -- 15. Time-dependent data : The problem -- When do we add the time dimension? -- Audit trails and snapshots : The basic audit trail approach ; Handling nonnumeric data ; The basic snapshot approach -- Sequences and versions -- Handling deletions -- Archiving -- Modeling time-dependent relationships : One-to-many relationships ; Many-to-many relationships ; Self-referencing relationships -- Date tables -- Temporal business rules -- Changes to the data structure -- Putting it into practice -- 16. Modeling for data warehouses and data marts : Characteristics of data warehouses and data marts : Data integration: working with existing databases ; Loads rather than updates ; Less predictable database "hits" ; Complex queries: Simple interface ; History ; Summarization -- Quality criteria for warehouse and mart models : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Simplicity and elegance ; Communication effectiveness ; Performance -- The basic design principle -- Modeling for the data warehouse : An initial model ; Understanding existing data ; Determining requirements ; Determining sources and dealing with differences ; Shaping data of data marts -- Modeling for the data mart : The basic challenge ; Multidimensional databases, stars and snowflakes ; Modeling time-dependent data -- 17. Enterprise data models and data management : : Data management : Problems of data management ; Managing data as a shared resource ; The evolution of data management -- Classification of existing data -- A target for planning -- A context for specifying new databases : Determining scope and interfaces ; Incorporating the enterprise data model in the development life cycle -- Guidance for database design -- Input to business planning -- Specification of an enterprise database -- Characteristics of enterprise data models -- Developing an enterprise data model : The development cycle ; Partitioning the task ; Inputs to the task ; Expertise requirements ; External standards -- Choice, creativity, and enterprise data models. |
| ctrlnum | (OCoLC)57371420 |
| dewey-full | 005.74 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.74 |
| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 3rd ed. |
| format | Electronic eBook |
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Witt.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">3rd ed.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Amsterdam ;</subfield><subfield code="a">Boston :</subfield><subfield code="b">Morgan Kaufmann Publishers,</subfield><subfield code="c">©2005.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2005</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xxvii, 532 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Part I. The basics : 1. What is data modeling? : A data-centered perspective -- A simple example -- Design, choice, and creativity -- Why is the data model important? : Leverage ; Conciseness ; Data quality -- What makes a good data model? : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Elegance ; Communication ; Integration ; Conflicting objectives -- Performance -- Database design stages and deliverables : Conceptual, logical, and physical data models ; The three-schema architecture and terminology -- Where do data models fit in? : Process-driven approaches ; Data driven approaches ; Parallel (blended) approaches ; Object-oriented approaches ; Prototyping approaches ; Agile methods -- Who should be involved in data modeling? -- Is data modeling still relevant? : Costs and benefits of data modeling ; Data modeling and packaged software ; Data integration ; Data warehouses ; Personal computing and user-developed systems ; Data modeling and XML -- Alternative approaches to data modeling -- Terminology -- 2. Basics of sound structure : An informal example of normalization -- Relational notation -- A more complex example -- Determining columns : One fact per column ; Hidden data ; Derivable data ; Determining the primary key -- Repeating groups and first normal form : Limit on maximum number of occurrences ; Data reusability and program complexity ; Recognizing repeating groups ; Removing repeating groups ; Determining the primary key of the new table ; First normal form -- Second and third normal forms : Problems with tables in first normal form ; Eliminating redundancy ; Determinants ; Third normal form -- Definitions and a few refinements : Determinants and functional dependency ; Primary keys ; Candidate keys ; A more formal definition of third normal form ; Foreign keys ; Referential integrity ; Update anomalies ; Denormalization and unnormalization ; Column and table names -- Choice, creativity, and normalization -- Terminology -- 3. The entity-relationship approach : A diagrammatic representation : The basic symbols: boxes and arrows ; Diagrammatic representation of foreign keys ; Interpreting the diagram ; Optionality ; Verifying the model ; Redundant arrows -- The top-down approach: entity-relationship modeling : Developing the diagram top down ; Terminology -- Entity classes : Entity diagramming convention ; Entity class naming ; Entity class definitions -- Relationships : Relationship diagramming conventions ; Many-to-many relationships ; One-to-one relationships ; Self-referencing relationships ; Relationships involving three or more entity classes ; Transferability ; Dependent and independent entity classes ; Relationship names -- Attributes : Attribute identification and definition ; Primary keys and the conceptual model -- Myths and folklore : Entity classes without relationships ; Allowed combinations of cardinality and optionality -- Creativity and E-R modeling -- 4. Subtypes and supertypes : Different levels of generalization -- Rules versus stability -- Using subtypes and supertypes as entity classes : Naming subtypes -- Diagramming conventions : Boxes in boxes ; UML conventions ; Using tools that do not support subtyping -- Definitions -- Attributes of supertypes and subtypes -- Nonoverlapping and exhaustive -- Overlapping subtypes and roles : Ignoring real-world overlaps ; Modeling only the supertypes ; Modeling the roles as participation in relationships ; Using role entity classes and one-to-one relationships ; Multiple partitions -- Hierarchy of subtypes -- Benefits of using subtypes and supertypes : Creativity ; Presentation: level of detail ; Communication ; Input to the design of views ; Classifying common patterns ; Divide and conquer -- When do we stop supertyping and subtyping? : Differences in identifiers ; Different attribute groups ; Different relationships ; Different processes ; Migration from one subtype to another ; Communication ; Capturing meaning and rules ; A pragmatic approach -- Generalization of relationships : Generalizing several one-to-many relationships to a single many-to-many relationship ; Generalizing several one-to-many relationships to a single one-to-many relationships ; Generalizing one-to-many and many-to-many relationships -- Theoretical background -- 5. Attributes and columns : Attribute definition ; Attribute disaggregation: One fact per attribute : Simple aggregation ; Conflated codes ; Meaningful ranges ; Inappropriate generalization -- Types of attributes : DBMS datatypes ; The attribute taxonomy in detail ; Attribute domains ; Column datatype and length requirements ; Conversion between external and internal representations -- Attribute names : Objectives of standardizing attribute names ; Some guidelines for attribute naming -- Attribute generalization : Options and trade-offs -- Attribute generalization resulting from entity generalization ; Attribute generalization with entity classes ; "First among equals" ; Limits to attribute generalizations -- 6. Primary keys and identity : Basic requirements and trade-offs -- Basic technical criteria : Applicability ; Uniqueness ; Minimality ; Stability -- Surrogate keys : Performance and programming issues ; Matching real-world identifiers ; Should surrogate keys be visible? ; Subtypes and surrogate keys -- Structured keys : When to use structured keys ; Programming and structured keys ; Running out of numbers -- Multiple candidate keys : Choosing a primary key ; Normalization issues -- Guidelines for choosing keys : Tables implementing independent entity classes ; Tables implementing dependent entity classes and many-to-many relationships -- Partially-null keys -- 7. Extensions and alternatives : Extensions to the basic E-R approach : Advanced attribute concepts -- The Chen E-R approach : The basic conventions ; Relationships with attributes ; Relationships involving three or more entity classes ; Roles ; The weak entity concept ; Chen conventions in practice -- Using UML object class diagrams : A conceptual data model in UML ; Advantages of UML -- Object role modeling.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Part II: Putting it all together : 8. Organizing the data modeling task : Data modeling in the real world -- Key issues in project organization -- Key issues in project organization : Recognition of data modeling ; Clear use of the data model ; Access to users and other business stakeholders ; Conceptual, logical, and physical models ; Cross-checking with the process model ; Appropriate tools -- Roles and responsibilities -- Partitioning large projects -- Maintaining the model : Examples of complex changes ; Managing change in the modeling process -- Packaging it up -- 9. The business requirements : Purpose of the requirements phase -- The business case -- Interviews and workshops : Should you model in interviews and workshops? ; Interviews with senior managers ; Interviews with subject matter experts ; Facilitated workshops -- Riding the trucks -- Existing systems and reverse engineering -- Process models -- Object class hierarchies : Classifying object classes ; A typical set of top-level object classes -- Developing an object class hierarchy -- Potential issues ; Advantages of the object class hierarchy technique -- 10. Conceptual data modeling : Designing real models -- Learning from designers in other disciplines -- Starting the modeling -- Pattern and generic models : Using patterns ; Using a generic model ; Adapting generic models from other applications ; Developing a generic model ; When there is not a generic model -- Bottom-up modeling -- Top-down modeling -- When the problem is too complex -- Hierarchies, networks, and chains : Hierarchies Networks (many-to-many relationships) ; Chains (one-to-one relationships) -- One-to-one relationships : Distinct real-world concepts ; Separating attribute groups ; Transferable one-to-one relationships ; Self-referencing one-to-one relationships ; Support for creativity -- Developing entity class definitions -- Handling exceptions -- The right attitude : Being aware ; Being creative ; Analyzing or designing ; Being brave ; Being understanding and understood -- Evaluating the model -- Direct review of data model diagrams -- Comparison with the process model ; Testing the model with sample data -- Prototypes -- The assertion approach : Naming conventions ; Rules for generating assertions -- 11. Logical database design : Overview of the transformations required -- Table specification : The standard transformation ; Exclusion of entity classes from the database ; Classification entity classes ; Many-to-many relationship implementation ; Relationships involving more than two entity classes ; Supertype/subtype implementation -- Basic column definition : Attribute implementation: the standard transformation ; Category attribute implementation ; Derivable attributes ; Attributes of relationships ; Complex attributes ; Multivalued attribute implementation ; Additional columns ; Column datatypes ; Column nullability -- Primary key specification -- Foreign key specification : One-to-many relationship implementation ; One-to-one relationship implementation ; Derivable relationships ; Optional relationships ; Overlapping foreign keys ; Split foreign keys -- Table and column names -- Logical data model notations -- 12. Physical database design : Inputs to database design -- Options available to the database designer -- Design decisions which do not affect program logic : Indexes ; Data storage ; Memory usage -- Crafting queries to run faster : Locking -- Logical schema decisions : Alternative implementation of relationships ; Table splitting ; Table merging ; Duplication ; Denormalization ; Ranges ; Hierarchies ; Integer storage of dates and times ; Additional tables -- Views : Views of supertypes and subtypes ; Inclusion of derived attributes in views ; Denormalization and views ; Views of split and merged tables.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Part III: Advanced topics : 13. Advanced normalization : Introduction to the higher normal forms : Common misconceptions -- Boyce-Codd normal form : Example of structure in 3NF but not in BCNF ; Definition of BCNF ; Enforcement of rules versus BCNF ; A note on domain key normal form -- Fourth normal form (4NF) and Fifth normal form (5NF) : Data in BCHF but not in 4NF ; Fifth normal form (5NF) ; Recognizing 4NF and 5NF situations ; Checking for 4NF and 5FN with the business specialist -- Beyond 5NF: splitting tables based on candidate keys -- Other normalization issues : Normalization and redundancy ; Reference tables produced by normalization ; Selecting the primary key after removing repeating groups ; Sequence of normalization and cross-table anomalies -- Advanced normalization in perspective -- 14. Modeling business rules : Types of business rules : Data rules ; Process rules ; What rules are relevant to the data modeler? -- Discovery and verification of business rules : Cardinality rules ; Other data validation rules ; Data derivation rules -- Documentation of business rules : Documentation in an E-R diagram ; Documenting other rules ; Use of subtypes to document rules -- Implementing business rules : Where to implement particular rules ; Implementation options: a detailed example ; Implementing mandatory relationships ; Referential integrity ; Restricting an attribute to a discrete set of values ; Rules involving multiple attributes ; Recording data that supports rules ; Rules that may be broken ; Enforcement of rules through primary key selection -- Rules of recursive relationships : Types of rules on recursive relationships ; Documenting rules on recursive relationships ; Implementing constraints on recursive relationships ; Analogous rules in many-to-many relationships -- 15. Time-dependent data : The problem -- When do we add the time dimension? -- Audit trails and snapshots : The basic audit trail approach ; Handling nonnumeric data ; The basic snapshot approach -- Sequences and versions -- Handling deletions -- Archiving -- Modeling time-dependent relationships : One-to-many relationships ; Many-to-many relationships ; Self-referencing relationships -- Date tables -- Temporal business rules -- Changes to the data structure -- Putting it into practice -- 16. Modeling for data warehouses and data marts : Characteristics of data warehouses and data marts : Data integration: working with existing databases ; Loads rather than updates ; Less predictable database "hits" ; Complex queries: Simple interface ; History ; Summarization -- Quality criteria for warehouse and mart models : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Simplicity and elegance ; Communication effectiveness ; Performance -- The basic design principle -- Modeling for the data warehouse : An initial model ; Understanding existing data ; Determining requirements ; Determining sources and dealing with differences ; Shaping data of data marts -- Modeling for the data mart : The basic challenge ; Multidimensional databases, stars and snowflakes ; Modeling time-dependent data -- 17. Enterprise data models and data management : : Data management : Problems of data management ; Managing data as a shared resource ; The evolution of data management -- Classification of existing data -- A target for planning -- A context for specifying new databases : Determining scope and interfaces ; Incorporating the enterprise data model in the development life cycle -- Guidance for database design -- Input to business planning -- Specification of an enterprise database -- Characteristics of enterprise data models -- Developing an enterprise data model : The development cycle ; Partitioning the task ; Inputs to the task ; Expertise requirements ; External standards -- Choice, creativity, and enterprise data models.</subfield></datafield><datafield tag="520" ind1="1" ind2=" "><subfield code="a">"This book provides expert tutelage for data modelers, business analysts, and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. 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| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:40Z |
| institution | BVB |
| isbn | 9780080488677 0080488676 1592782515 9781592782512 1417561564 9781417561568 9780126445510 0126445516 |
| language | English |
| oclc_num | 57371420 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xxvii, 532 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2005 |
| publishDateSearch | 2005 |
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| publisher | Morgan Kaufmann Publishers, |
| record_format | marc |
| spelling | Simsion, Graeme C. https://id.oclc.org/worldcat/entity/E39PBJrm8F4w3y3HKpPh7KH3cP http://id.loc.gov/authorities/names/n93096585 Data modeling essentials / Graeme C. Simsion and Graham C. Witt. 3rd ed. Amsterdam ; Boston : Morgan Kaufmann Publishers, ©2005. ©2005 1 online resource (xxvii, 532 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. Part I. The basics : 1. What is data modeling? : A data-centered perspective -- A simple example -- Design, choice, and creativity -- Why is the data model important? : Leverage ; Conciseness ; Data quality -- What makes a good data model? : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Elegance ; Communication ; Integration ; Conflicting objectives -- Performance -- Database design stages and deliverables : Conceptual, logical, and physical data models ; The three-schema architecture and terminology -- Where do data models fit in? : Process-driven approaches ; Data driven approaches ; Parallel (blended) approaches ; Object-oriented approaches ; Prototyping approaches ; Agile methods -- Who should be involved in data modeling? -- Is data modeling still relevant? : Costs and benefits of data modeling ; Data modeling and packaged software ; Data integration ; Data warehouses ; Personal computing and user-developed systems ; Data modeling and XML -- Alternative approaches to data modeling -- Terminology -- 2. Basics of sound structure : An informal example of normalization -- Relational notation -- A more complex example -- Determining columns : One fact per column ; Hidden data ; Derivable data ; Determining the primary key -- Repeating groups and first normal form : Limit on maximum number of occurrences ; Data reusability and program complexity ; Recognizing repeating groups ; Removing repeating groups ; Determining the primary key of the new table ; First normal form -- Second and third normal forms : Problems with tables in first normal form ; Eliminating redundancy ; Determinants ; Third normal form -- Definitions and a few refinements : Determinants and functional dependency ; Primary keys ; Candidate keys ; A more formal definition of third normal form ; Foreign keys ; Referential integrity ; Update anomalies ; Denormalization and unnormalization ; Column and table names -- Choice, creativity, and normalization -- Terminology -- 3. The entity-relationship approach : A diagrammatic representation : The basic symbols: boxes and arrows ; Diagrammatic representation of foreign keys ; Interpreting the diagram ; Optionality ; Verifying the model ; Redundant arrows -- The top-down approach: entity-relationship modeling : Developing the diagram top down ; Terminology -- Entity classes : Entity diagramming convention ; Entity class naming ; Entity class definitions -- Relationships : Relationship diagramming conventions ; Many-to-many relationships ; One-to-one relationships ; Self-referencing relationships ; Relationships involving three or more entity classes ; Transferability ; Dependent and independent entity classes ; Relationship names -- Attributes : Attribute identification and definition ; Primary keys and the conceptual model -- Myths and folklore : Entity classes without relationships ; Allowed combinations of cardinality and optionality -- Creativity and E-R modeling -- 4. Subtypes and supertypes : Different levels of generalization -- Rules versus stability -- Using subtypes and supertypes as entity classes : Naming subtypes -- Diagramming conventions : Boxes in boxes ; UML conventions ; Using tools that do not support subtyping -- Definitions -- Attributes of supertypes and subtypes -- Nonoverlapping and exhaustive -- Overlapping subtypes and roles : Ignoring real-world overlaps ; Modeling only the supertypes ; Modeling the roles as participation in relationships ; Using role entity classes and one-to-one relationships ; Multiple partitions -- Hierarchy of subtypes -- Benefits of using subtypes and supertypes : Creativity ; Presentation: level of detail ; Communication ; Input to the design of views ; Classifying common patterns ; Divide and conquer -- When do we stop supertyping and subtyping? : Differences in identifiers ; Different attribute groups ; Different relationships ; Different processes ; Migration from one subtype to another ; Communication ; Capturing meaning and rules ; A pragmatic approach -- Generalization of relationships : Generalizing several one-to-many relationships to a single many-to-many relationship ; Generalizing several one-to-many relationships to a single one-to-many relationships ; Generalizing one-to-many and many-to-many relationships -- Theoretical background -- 5. Attributes and columns : Attribute definition ; Attribute disaggregation: One fact per attribute : Simple aggregation ; Conflated codes ; Meaningful ranges ; Inappropriate generalization -- Types of attributes : DBMS datatypes ; The attribute taxonomy in detail ; Attribute domains ; Column datatype and length requirements ; Conversion between external and internal representations -- Attribute names : Objectives of standardizing attribute names ; Some guidelines for attribute naming -- Attribute generalization : Options and trade-offs -- Attribute generalization resulting from entity generalization ; Attribute generalization with entity classes ; "First among equals" ; Limits to attribute generalizations -- 6. Primary keys and identity : Basic requirements and trade-offs -- Basic technical criteria : Applicability ; Uniqueness ; Minimality ; Stability -- Surrogate keys : Performance and programming issues ; Matching real-world identifiers ; Should surrogate keys be visible? ; Subtypes and surrogate keys -- Structured keys : When to use structured keys ; Programming and structured keys ; Running out of numbers -- Multiple candidate keys : Choosing a primary key ; Normalization issues -- Guidelines for choosing keys : Tables implementing independent entity classes ; Tables implementing dependent entity classes and many-to-many relationships -- Partially-null keys -- 7. Extensions and alternatives : Extensions to the basic E-R approach : Advanced attribute concepts -- The Chen E-R approach : The basic conventions ; Relationships with attributes ; Relationships involving three or more entity classes ; Roles ; The weak entity concept ; Chen conventions in practice -- Using UML object class diagrams : A conceptual data model in UML ; Advantages of UML -- Object role modeling. Part II: Putting it all together : 8. Organizing the data modeling task : Data modeling in the real world -- Key issues in project organization -- Key issues in project organization : Recognition of data modeling ; Clear use of the data model ; Access to users and other business stakeholders ; Conceptual, logical, and physical models ; Cross-checking with the process model ; Appropriate tools -- Roles and responsibilities -- Partitioning large projects -- Maintaining the model : Examples of complex changes ; Managing change in the modeling process -- Packaging it up -- 9. The business requirements : Purpose of the requirements phase -- The business case -- Interviews and workshops : Should you model in interviews and workshops? ; Interviews with senior managers ; Interviews with subject matter experts ; Facilitated workshops -- Riding the trucks -- Existing systems and reverse engineering -- Process models -- Object class hierarchies : Classifying object classes ; A typical set of top-level object classes -- Developing an object class hierarchy -- Potential issues ; Advantages of the object class hierarchy technique -- 10. Conceptual data modeling : Designing real models -- Learning from designers in other disciplines -- Starting the modeling -- Pattern and generic models : Using patterns ; Using a generic model ; Adapting generic models from other applications ; Developing a generic model ; When there is not a generic model -- Bottom-up modeling -- Top-down modeling -- When the problem is too complex -- Hierarchies, networks, and chains : Hierarchies Networks (many-to-many relationships) ; Chains (one-to-one relationships) -- One-to-one relationships : Distinct real-world concepts ; Separating attribute groups ; Transferable one-to-one relationships ; Self-referencing one-to-one relationships ; Support for creativity -- Developing entity class definitions -- Handling exceptions -- The right attitude : Being aware ; Being creative ; Analyzing or designing ; Being brave ; Being understanding and understood -- Evaluating the model -- Direct review of data model diagrams -- Comparison with the process model ; Testing the model with sample data -- Prototypes -- The assertion approach : Naming conventions ; Rules for generating assertions -- 11. Logical database design : Overview of the transformations required -- Table specification : The standard transformation ; Exclusion of entity classes from the database ; Classification entity classes ; Many-to-many relationship implementation ; Relationships involving more than two entity classes ; Supertype/subtype implementation -- Basic column definition : Attribute implementation: the standard transformation ; Category attribute implementation ; Derivable attributes ; Attributes of relationships ; Complex attributes ; Multivalued attribute implementation ; Additional columns ; Column datatypes ; Column nullability -- Primary key specification -- Foreign key specification : One-to-many relationship implementation ; One-to-one relationship implementation ; Derivable relationships ; Optional relationships ; Overlapping foreign keys ; Split foreign keys -- Table and column names -- Logical data model notations -- 12. Physical database design : Inputs to database design -- Options available to the database designer -- Design decisions which do not affect program logic : Indexes ; Data storage ; Memory usage -- Crafting queries to run faster : Locking -- Logical schema decisions : Alternative implementation of relationships ; Table splitting ; Table merging ; Duplication ; Denormalization ; Ranges ; Hierarchies ; Integer storage of dates and times ; Additional tables -- Views : Views of supertypes and subtypes ; Inclusion of derived attributes in views ; Denormalization and views ; Views of split and merged tables. Part III: Advanced topics : 13. Advanced normalization : Introduction to the higher normal forms : Common misconceptions -- Boyce-Codd normal form : Example of structure in 3NF but not in BCNF ; Definition of BCNF ; Enforcement of rules versus BCNF ; A note on domain key normal form -- Fourth normal form (4NF) and Fifth normal form (5NF) : Data in BCHF but not in 4NF ; Fifth normal form (5NF) ; Recognizing 4NF and 5NF situations ; Checking for 4NF and 5FN with the business specialist -- Beyond 5NF: splitting tables based on candidate keys -- Other normalization issues : Normalization and redundancy ; Reference tables produced by normalization ; Selecting the primary key after removing repeating groups ; Sequence of normalization and cross-table anomalies -- Advanced normalization in perspective -- 14. Modeling business rules : Types of business rules : Data rules ; Process rules ; What rules are relevant to the data modeler? -- Discovery and verification of business rules : Cardinality rules ; Other data validation rules ; Data derivation rules -- Documentation of business rules : Documentation in an E-R diagram ; Documenting other rules ; Use of subtypes to document rules -- Implementing business rules : Where to implement particular rules ; Implementation options: a detailed example ; Implementing mandatory relationships ; Referential integrity ; Restricting an attribute to a discrete set of values ; Rules involving multiple attributes ; Recording data that supports rules ; Rules that may be broken ; Enforcement of rules through primary key selection -- Rules of recursive relationships : Types of rules on recursive relationships ; Documenting rules on recursive relationships ; Implementing constraints on recursive relationships ; Analogous rules in many-to-many relationships -- 15. Time-dependent data : The problem -- When do we add the time dimension? -- Audit trails and snapshots : The basic audit trail approach ; Handling nonnumeric data ; The basic snapshot approach -- Sequences and versions -- Handling deletions -- Archiving -- Modeling time-dependent relationships : One-to-many relationships ; Many-to-many relationships ; Self-referencing relationships -- Date tables -- Temporal business rules -- Changes to the data structure -- Putting it into practice -- 16. Modeling for data warehouses and data marts : Characteristics of data warehouses and data marts : Data integration: working with existing databases ; Loads rather than updates ; Less predictable database "hits" ; Complex queries: Simple interface ; History ; Summarization -- Quality criteria for warehouse and mart models : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Simplicity and elegance ; Communication effectiveness ; Performance -- The basic design principle -- Modeling for the data warehouse : An initial model ; Understanding existing data ; Determining requirements ; Determining sources and dealing with differences ; Shaping data of data marts -- Modeling for the data mart : The basic challenge ; Multidimensional databases, stars and snowflakes ; Modeling time-dependent data -- 17. Enterprise data models and data management : : Data management : Problems of data management ; Managing data as a shared resource ; The evolution of data management -- Classification of existing data -- A target for planning -- A context for specifying new databases : Determining scope and interfaces ; Incorporating the enterprise data model in the development life cycle -- Guidance for database design -- Input to business planning -- Specification of an enterprise database -- Characteristics of enterprise data models -- Developing an enterprise data model : The development cycle ; Partitioning the task ; Inputs to the task ; Expertise requirements ; External standards -- Choice, creativity, and enterprise data models. "This book provides expert tutelage for data modelers, business analysts, and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters delve into advanced subjects, including business rules, data warehousing, and enterprise-wide modeling and data management."--Jacket. Database design. http://id.loc.gov/authorities/subjects/sh88004472 Data structures (Computer science) http://id.loc.gov/authorities/subjects/sh85035862 Bases de données Conception. Structures de données (Informatique) COMPUTERS Desktop Applications Databases. bisacsh COMPUTERS Database Management General. bisacsh COMPUTERS System Administration Storage & Retrieval. bisacsh Data structures (Computer science) fast Database design fast Datenbankentwurf gnd http://d-nb.info/gnd/4127613-9 Datenmodell gnd http://d-nb.info/gnd/4192516-6 Gegevensmodellering. gtt Witt, Graham C. https://id.oclc.org/worldcat/entity/E39PCjF46RbBt9Gc8tBy4HYbgq http://id.loc.gov/authorities/names/n2001005052 has work: Data modeling essentials (Text) https://id.oclc.org/worldcat/entity/E39PCGVvp9jHYPKXYHX93qtrmd https://id.oclc.org/worldcat/ontology/hasWork Print version: Simsion, Graeme C. Data modeling essentials. 3rd ed. Amsterdam ; Boston : Morgan Kaufmann Publishers, ©2005 (DLC) 2004275106 (OCoLC)57010213 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=117175 Volltext |
| spellingShingle | Simsion, Graeme C. Data modeling essentials / Part I. The basics : 1. What is data modeling? : A data-centered perspective -- A simple example -- Design, choice, and creativity -- Why is the data model important? : Leverage ; Conciseness ; Data quality -- What makes a good data model? : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Elegance ; Communication ; Integration ; Conflicting objectives -- Performance -- Database design stages and deliverables : Conceptual, logical, and physical data models ; The three-schema architecture and terminology -- Where do data models fit in? : Process-driven approaches ; Data driven approaches ; Parallel (blended) approaches ; Object-oriented approaches ; Prototyping approaches ; Agile methods -- Who should be involved in data modeling? -- Is data modeling still relevant? : Costs and benefits of data modeling ; Data modeling and packaged software ; Data integration ; Data warehouses ; Personal computing and user-developed systems ; Data modeling and XML -- Alternative approaches to data modeling -- Terminology -- 2. Basics of sound structure : An informal example of normalization -- Relational notation -- A more complex example -- Determining columns : One fact per column ; Hidden data ; Derivable data ; Determining the primary key -- Repeating groups and first normal form : Limit on maximum number of occurrences ; Data reusability and program complexity ; Recognizing repeating groups ; Removing repeating groups ; Determining the primary key of the new table ; First normal form -- Second and third normal forms : Problems with tables in first normal form ; Eliminating redundancy ; Determinants ; Third normal form -- Definitions and a few refinements : Determinants and functional dependency ; Primary keys ; Candidate keys ; A more formal definition of third normal form ; Foreign keys ; Referential integrity ; Update anomalies ; Denormalization and unnormalization ; Column and table names -- Choice, creativity, and normalization -- Terminology -- 3. The entity-relationship approach : A diagrammatic representation : The basic symbols: boxes and arrows ; Diagrammatic representation of foreign keys ; Interpreting the diagram ; Optionality ; Verifying the model ; Redundant arrows -- The top-down approach: entity-relationship modeling : Developing the diagram top down ; Terminology -- Entity classes : Entity diagramming convention ; Entity class naming ; Entity class definitions -- Relationships : Relationship diagramming conventions ; Many-to-many relationships ; One-to-one relationships ; Self-referencing relationships ; Relationships involving three or more entity classes ; Transferability ; Dependent and independent entity classes ; Relationship names -- Attributes : Attribute identification and definition ; Primary keys and the conceptual model -- Myths and folklore : Entity classes without relationships ; Allowed combinations of cardinality and optionality -- Creativity and E-R modeling -- 4. Subtypes and supertypes : Different levels of generalization -- Rules versus stability -- Using subtypes and supertypes as entity classes : Naming subtypes -- Diagramming conventions : Boxes in boxes ; UML conventions ; Using tools that do not support subtyping -- Definitions -- Attributes of supertypes and subtypes -- Nonoverlapping and exhaustive -- Overlapping subtypes and roles : Ignoring real-world overlaps ; Modeling only the supertypes ; Modeling the roles as participation in relationships ; Using role entity classes and one-to-one relationships ; Multiple partitions -- Hierarchy of subtypes -- Benefits of using subtypes and supertypes : Creativity ; Presentation: level of detail ; Communication ; Input to the design of views ; Classifying common patterns ; Divide and conquer -- When do we stop supertyping and subtyping? : Differences in identifiers ; Different attribute groups ; Different relationships ; Different processes ; Migration from one subtype to another ; Communication ; Capturing meaning and rules ; A pragmatic approach -- Generalization of relationships : Generalizing several one-to-many relationships to a single many-to-many relationship ; Generalizing several one-to-many relationships to a single one-to-many relationships ; Generalizing one-to-many and many-to-many relationships -- Theoretical background -- 5. Attributes and columns : Attribute definition ; Attribute disaggregation: One fact per attribute : Simple aggregation ; Conflated codes ; Meaningful ranges ; Inappropriate generalization -- Types of attributes : DBMS datatypes ; The attribute taxonomy in detail ; Attribute domains ; Column datatype and length requirements ; Conversion between external and internal representations -- Attribute names : Objectives of standardizing attribute names ; Some guidelines for attribute naming -- Attribute generalization : Options and trade-offs -- Attribute generalization resulting from entity generalization ; Attribute generalization with entity classes ; "First among equals" ; Limits to attribute generalizations -- 6. Primary keys and identity : Basic requirements and trade-offs -- Basic technical criteria : Applicability ; Uniqueness ; Minimality ; Stability -- Surrogate keys : Performance and programming issues ; Matching real-world identifiers ; Should surrogate keys be visible? ; Subtypes and surrogate keys -- Structured keys : When to use structured keys ; Programming and structured keys ; Running out of numbers -- Multiple candidate keys : Choosing a primary key ; Normalization issues -- Guidelines for choosing keys : Tables implementing independent entity classes ; Tables implementing dependent entity classes and many-to-many relationships -- Partially-null keys -- 7. Extensions and alternatives : Extensions to the basic E-R approach : Advanced attribute concepts -- The Chen E-R approach : The basic conventions ; Relationships with attributes ; Relationships involving three or more entity classes ; Roles ; The weak entity concept ; Chen conventions in practice -- Using UML object class diagrams : A conceptual data model in UML ; Advantages of UML -- Object role modeling. Part II: Putting it all together : 8. Organizing the data modeling task : Data modeling in the real world -- Key issues in project organization -- Key issues in project organization : Recognition of data modeling ; Clear use of the data model ; Access to users and other business stakeholders ; Conceptual, logical, and physical models ; Cross-checking with the process model ; Appropriate tools -- Roles and responsibilities -- Partitioning large projects -- Maintaining the model : Examples of complex changes ; Managing change in the modeling process -- Packaging it up -- 9. The business requirements : Purpose of the requirements phase -- The business case -- Interviews and workshops : Should you model in interviews and workshops? ; Interviews with senior managers ; Interviews with subject matter experts ; Facilitated workshops -- Riding the trucks -- Existing systems and reverse engineering -- Process models -- Object class hierarchies : Classifying object classes ; A typical set of top-level object classes -- Developing an object class hierarchy -- Potential issues ; Advantages of the object class hierarchy technique -- 10. Conceptual data modeling : Designing real models -- Learning from designers in other disciplines -- Starting the modeling -- Pattern and generic models : Using patterns ; Using a generic model ; Adapting generic models from other applications ; Developing a generic model ; When there is not a generic model -- Bottom-up modeling -- Top-down modeling -- When the problem is too complex -- Hierarchies, networks, and chains : Hierarchies Networks (many-to-many relationships) ; Chains (one-to-one relationships) -- One-to-one relationships : Distinct real-world concepts ; Separating attribute groups ; Transferable one-to-one relationships ; Self-referencing one-to-one relationships ; Support for creativity -- Developing entity class definitions -- Handling exceptions -- The right attitude : Being aware ; Being creative ; Analyzing or designing ; Being brave ; Being understanding and understood -- Evaluating the model -- Direct review of data model diagrams -- Comparison with the process model ; Testing the model with sample data -- Prototypes -- The assertion approach : Naming conventions ; Rules for generating assertions -- 11. Logical database design : Overview of the transformations required -- Table specification : The standard transformation ; Exclusion of entity classes from the database ; Classification entity classes ; Many-to-many relationship implementation ; Relationships involving more than two entity classes ; Supertype/subtype implementation -- Basic column definition : Attribute implementation: the standard transformation ; Category attribute implementation ; Derivable attributes ; Attributes of relationships ; Complex attributes ; Multivalued attribute implementation ; Additional columns ; Column datatypes ; Column nullability -- Primary key specification -- Foreign key specification : One-to-many relationship implementation ; One-to-one relationship implementation ; Derivable relationships ; Optional relationships ; Overlapping foreign keys ; Split foreign keys -- Table and column names -- Logical data model notations -- 12. Physical database design : Inputs to database design -- Options available to the database designer -- Design decisions which do not affect program logic : Indexes ; Data storage ; Memory usage -- Crafting queries to run faster : Locking -- Logical schema decisions : Alternative implementation of relationships ; Table splitting ; Table merging ; Duplication ; Denormalization ; Ranges ; Hierarchies ; Integer storage of dates and times ; Additional tables -- Views : Views of supertypes and subtypes ; Inclusion of derived attributes in views ; Denormalization and views ; Views of split and merged tables. Part III: Advanced topics : 13. Advanced normalization : Introduction to the higher normal forms : Common misconceptions -- Boyce-Codd normal form : Example of structure in 3NF but not in BCNF ; Definition of BCNF ; Enforcement of rules versus BCNF ; A note on domain key normal form -- Fourth normal form (4NF) and Fifth normal form (5NF) : Data in BCHF but not in 4NF ; Fifth normal form (5NF) ; Recognizing 4NF and 5NF situations ; Checking for 4NF and 5FN with the business specialist -- Beyond 5NF: splitting tables based on candidate keys -- Other normalization issues : Normalization and redundancy ; Reference tables produced by normalization ; Selecting the primary key after removing repeating groups ; Sequence of normalization and cross-table anomalies -- Advanced normalization in perspective -- 14. Modeling business rules : Types of business rules : Data rules ; Process rules ; What rules are relevant to the data modeler? -- Discovery and verification of business rules : Cardinality rules ; Other data validation rules ; Data derivation rules -- Documentation of business rules : Documentation in an E-R diagram ; Documenting other rules ; Use of subtypes to document rules -- Implementing business rules : Where to implement particular rules ; Implementation options: a detailed example ; Implementing mandatory relationships ; Referential integrity ; Restricting an attribute to a discrete set of values ; Rules involving multiple attributes ; Recording data that supports rules ; Rules that may be broken ; Enforcement of rules through primary key selection -- Rules of recursive relationships : Types of rules on recursive relationships ; Documenting rules on recursive relationships ; Implementing constraints on recursive relationships ; Analogous rules in many-to-many relationships -- 15. Time-dependent data : The problem -- When do we add the time dimension? -- Audit trails and snapshots : The basic audit trail approach ; Handling nonnumeric data ; The basic snapshot approach -- Sequences and versions -- Handling deletions -- Archiving -- Modeling time-dependent relationships : One-to-many relationships ; Many-to-many relationships ; Self-referencing relationships -- Date tables -- Temporal business rules -- Changes to the data structure -- Putting it into practice -- 16. Modeling for data warehouses and data marts : Characteristics of data warehouses and data marts : Data integration: working with existing databases ; Loads rather than updates ; Less predictable database "hits" ; Complex queries: Simple interface ; History ; Summarization -- Quality criteria for warehouse and mart models : Completeness ; Nonredundancy ; Enforcement of business rules ; Data reusability ; Stability and flexibility ; Simplicity and elegance ; Communication effectiveness ; Performance -- The basic design principle -- Modeling for the data warehouse : An initial model ; Understanding existing data ; Determining requirements ; Determining sources and dealing with differences ; Shaping data of data marts -- Modeling for the data mart : The basic challenge ; Multidimensional databases, stars and snowflakes ; Modeling time-dependent data -- 17. Enterprise data models and data management : : Data management : Problems of data management ; Managing data as a shared resource ; The evolution of data management -- Classification of existing data -- A target for planning -- A context for specifying new databases : Determining scope and interfaces ; Incorporating the enterprise data model in the development life cycle -- Guidance for database design -- Input to business planning -- Specification of an enterprise database -- Characteristics of enterprise data models -- Developing an enterprise data model : The development cycle ; Partitioning the task ; Inputs to the task ; Expertise requirements ; External standards -- Choice, creativity, and enterprise data models. Database design. http://id.loc.gov/authorities/subjects/sh88004472 Data structures (Computer science) http://id.loc.gov/authorities/subjects/sh85035862 Bases de données Conception. Structures de données (Informatique) COMPUTERS Desktop Applications Databases. bisacsh COMPUTERS Database Management General. bisacsh COMPUTERS System Administration Storage & Retrieval. bisacsh Data structures (Computer science) fast Database design fast Datenbankentwurf gnd http://d-nb.info/gnd/4127613-9 Datenmodell gnd http://d-nb.info/gnd/4192516-6 Gegevensmodellering. gtt |
| subject_GND | http://id.loc.gov/authorities/subjects/sh88004472 http://id.loc.gov/authorities/subjects/sh85035862 http://d-nb.info/gnd/4127613-9 http://d-nb.info/gnd/4192516-6 |
| title | Data modeling essentials / |
| title_auth | Data modeling essentials / |
| title_exact_search | Data modeling essentials / |
| title_full | Data modeling essentials / Graeme C. Simsion and Graham C. Witt. |
| title_fullStr | Data modeling essentials / Graeme C. Simsion and Graham C. Witt. |
| title_full_unstemmed | Data modeling essentials / Graeme C. Simsion and Graham C. Witt. |
| title_short | Data modeling essentials / |
| title_sort | data modeling essentials |
| topic | Database design. http://id.loc.gov/authorities/subjects/sh88004472 Data structures (Computer science) http://id.loc.gov/authorities/subjects/sh85035862 Bases de données Conception. Structures de données (Informatique) COMPUTERS Desktop Applications Databases. bisacsh COMPUTERS Database Management General. bisacsh COMPUTERS System Administration Storage & Retrieval. bisacsh Data structures (Computer science) fast Database design fast Datenbankentwurf gnd http://d-nb.info/gnd/4127613-9 Datenmodell gnd http://d-nb.info/gnd/4192516-6 Gegevensmodellering. gtt |
| topic_facet | Database design. Data structures (Computer science) Bases de données Conception. Structures de données (Informatique) COMPUTERS Desktop Applications Databases. COMPUTERS Database Management General. COMPUTERS System Administration Storage & Retrieval. Database design Datenbankentwurf Datenmodell Gegevensmodellering. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=117175 |
| work_keys_str_mv | AT simsiongraemec datamodelingessentials AT wittgrahamc datamodelingessentials |