PowerPoint PresentationChapter Seven Data Modeling and AnalysisChapter MapSystem ModelsData ModelingSample Entity Relationship Diagram (ERD)Data Modeling Concepts: EntitySlide 8Data Modeling Concepts: AttributesData Modeling Concepts: DomainsData Modeling Concepts: IdentificationSlide 12Data Modeling Concepts: Identification Keys & Subsetting CriteriaData Modeling Concepts: RelationshipsData Modeling Concepts: CardinalityData Modeling Concepts: DegreeSlide 17Slide 18Slide 19Data Modeling Concepts: Foreign KeysSlide 21Slide 22Slide 23Resolving Nonspecific Relationships (continued)Slide 25Important TermsData Modeling Concepts: GeneralizationGeneralization HierarchyLogical Data ModelingLogical Data ModelingEntity Discovery for SoundStageThe Context Data ModelKeysCodesThe Key-based Data ModelThe Key-based Data Model With GeneralizationThe Fully-Attributed Data Model – p 287Data Analysis & NormalizationNormalization: 1NF, 2NF, 3NFFirst Normal Form ExampleSlide 41Slide 42Second Normal Form ExampleSlide 44Slide 45Third Normal Form ExampleSlide 47SoundStage 3NF Data ModelData-to-Location-CRUD MatrixC H A P T E R7DATA MODELING AND ANALYSISChapter Seven Data Modeling and AnalysisDefine systems modeling and differentiate between logical and physical system models.Define data modeling and explain its benefits.Recognize and understand the basic concepts and constructs of a data model.Read and interpret an entity relationship data model.Explain when data models are constructed during a project and where the models are stored.Discover entities and relationships.Construct an entity-relationship context diagram.Discover or invent keys for entities and construct a key-based diagram.Construct a fully attributed entity relationship diagram and describe all data structures and attributes to the repository or encyclopedia.Normalize a logical data model to remove impurities that can make a database unstable, inflexible, and nonscalable. Describe a useful tool for mapping data requirements to business operating locations.Chapter MapSystem ModelsA model is a representation of reality. Logical models show what a system is or does. Implementation independentPhysical models show not only what a system is or does how the system is physically and technically implemented.Data ModelingData modeling –technique for organizing and documenting a system’s data. –sometimes called database modeling •eventually implemented as a database. –sometimes called information modeling. actual model is frequently called an entity relationship diagram (ERD) –it depicts data in terms of the entities and relationshipsSample Entity Relationship Diagram (ERD)Persons: agency, contractor, customer, department, division, employee, instructor, student, supplier. Places: sales region, building, room, branch office, campus. Objects: book, machine, part, product, raw material, software license, software package, tool, vehicle model, vehicle. Events: application, award, cancellation, class, flight, invoice, order, registration, renewal, requisition, reservation, sale, trip. Concepts: account, block of time, bond, course, fund, qualification, stock. Name of EntityData Modeling Concepts: EntityAn entity is a class of persons, places, objects, events, or concepts about which we need to capture and store data.Betty ArnoldJohn TaylorLisa SimmonsBill MacyHeather LeathTim WrenchData Modeling Concepts: EntityAn entity instance is a single occurrence of an entity. Example: instances of the entity STUDENT may includeSTUDENTSTUDENTNa me.Last Name.First Name.Middle InitialAddress.Street Address.City.State or Province.Country.Postal CodePhone Number.Area Code.Exchange Number.Number Within ExchangeDate of BirthGenderRaceMajorGrade Poin t AverageData Modeling Concepts: AttributesAn attribute is a descriptive property or characteristic of an entity. Synonyms include element, property, and field. A compound attribute is one that actually consists of other attributesData Modeling Concepts: DomainsThe data type for an attribute –defines what type of data can be stored in that attribute. The domain of an attribute –defines what values an attribute can legitimately take on.The default value for an attribute –is the value that will be recorded if not specified by the user.Data Modeling Concepts: IdentificationA key –an attribute, or a group of attributes, that assumes a unique value for each entity instance. A concatenated key – group of attributes that uniquely identifies an instance of an entity A candidate key –A “candidate to become the primary key” of instances of an entity. A primary key –That candidate key that will most commonly be used to uniquely identify a single entity instance. An alternate key –A candidate key that is not selected to become the primary key A subsetting criteria –an attribute (or concatenated attribute) whose finite values divide all entity instances into useful subsets.STUDENTStudent NumberSocial Security NumberName.Last Name.First Name.Middle InitialAddress.Street Address.City.State or Province.Country.Postal CodePhone Number.Area Code.Exchange Number.Number Within ExchangeDate of BirthGenderRaceMajorGrade Point AverageIdentify: Candidate Keys Primary Keys Alternate Keys Concatenated Keys Subsetting AttributesData Modeling Concepts: Identification Keys & Subsetting CriteriaSTUDENTStudent Number (Primary Key)Social Security Number (Alternate Key)Name.Last Name.First Name.Middle InitialAddress.Street Address.City.State or Province.Country.Postal CodePhone Number.Area Code.Exchange Number.Number Within ExchangeDate of BirthGender (Subsetting Criteria 1)Race (Subsetting Criteria 2)Major (Subsetting Criteria 3)Grade Po int AverageData Modeling Concepts: RelationshipsA relationship is a natural business association that exists between one or more entities. The relationship may represent an event that links the entities or merely a logical affinity that exists between the entities.bidirectionalData Modeling Concepts: CardinalityCardinality defines the minimum and maximum number of occurrences of one entity that may be related to a single occurrence of the other entity. Because all relationships are bidirectional, cardinality must be defined in both directions for every relationship. One or manyZero or manyData Modeling Concepts: DegreeThe degree of a relationship is the number of entities