Database 3   Conceptual Modeling And Er
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Database 3 Conceptual Modeling And Er

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Database 3   Conceptual Modeling And Er Database 3 Conceptual Modeling And Er Presentation Transcript

    • In previous section
      • General steps in DB development process , ISA ,EDM
      • Database Architectures
    • Aim
    • Design of conceptual Data Model
    • Objectives
    • Understand
      • Conceptual Data Modeling
      • Concepts of E-R Modeling
      • Model Example & Limitations
    RECAP
    • Purpose:
      • create detailed specification of internal documents and tasks from the EDM
    • Input:
      • EDM, usage statistics, and other information gathered during the analysis
    • Output:
      • ER-Diagram, Data Representation, Constraints, Task Decompositions
    • Techniques:
      • data modeling
      • top-down decomposition of tasks until their specification is sufficiently detailed to allow a programmer to implement them
      • task decomposition may result in tasks replacing the original task or in subtasks controlled by the original task
    • Tools: ER-Model
    APPROACH
    • To build a
    • What is 1 st Step ?
    • What are the choices,Why ?
    • User Requirements
    • - User is not sure
    • - Precise specification is difficult
    • Communication Problems
    • - Analyst & user communication
    • - User not willing to take responsibility
    • Technical
      • - Skills of modeler
      • - Design Methodology – Non Standard
    CONCEPTUAL MODEL
  • Feasibility study Requirement Analysis & Specifications Data Modeling Process Modeling Implementation Prototyping Testing DESIGN APPROACH
  • DESIGN PHASES Requirements Collection & Analysis Conceptual Design (G) Logical Design (Blue Print) Physical Design Application Design Methods Req. Collection Conceptual Model Data Base System DBMS Tools , OS Requirements Specs Conceptual Database Model Logical Models Performance Tests Final Schema Application PGMS
  • Conceptual Design Logical Design Physical Design Application Design Requirement Collection & Analysis C,Windows,Power Builder ORACLE Relational DBs ER & ERR Model Questionnaire & Interview DESIGN METHODS
    • Models can be useful when we want to examine or manage part of the real world
    • The costs of using a model are often considerably lower than the costs of using or experimenting with the real world itself
    • Examples:
      • airplane simulator
      • nuclear power plant simulator
      • flood warning system
      • model of US economy
      • model of a heat reservoir
      • map
    WHY TO USE MODEL
  • A Map Is a Model of Reality MODEL OF REALITY
    • A model is a means of communication
    • Users of a model must have a certain amount of knowledge in common
    • A model on emphasized selected aspects
    • A model is described in some language
    • A model can be erroneous
    • A message to map makers: “Highways are not painted red, rivers don’t have county lines running down the middle, and you can’t see contour lines on a mountain” [Kent 78]
    MODEL TO RAISE QUESTION
      • A process that construct an abstract model which represents the entities,relationships and activities of an enterprise of real world
      • Putting a Real World Object on to Paper
    Purpose of model is to sharpen the question MYCOM.COM    US $ 10 b
    MCA/MMS IIPS CONCEPTUAL MODELING
    • Why Conceptual Modeling ?
    • Obtain better understanding of business
    • Enable the end-user communication
    • Discover design errors at early stage
    • Build a Solid Foundation
    • Ensure the quality
    • A DBMS independent DB design
    • What to Model ?
    • Static information
      • Data - Entities
      • Associations - Relationship among entities
    • Dynamic Information
      • Process – operations/transactions
      • Integrity constraints – Business Rules / Regulations
    CONCEPTUAL MODELING
    • Process - Oriented Approach
      • Focus on activities, process & operations
      • Data Flow Diagram
    • Data – Oriented Approach
      • Focus on data & their relationship
      • Characteristics of Data captured
      • Data more complex than process
      • Rich data source is the GOAL
      • Data is more stable than process
      • Data Orientation - Longer life
    • Object – Oriented Approach
      • Combine data and process
    CONCEPTUAL MODELING APPROACHES
    • Entity – relationship model (ER) introduced in 1976 by Peter Chen
    • Extended ER model (EER) expanded the original ER with new concepts
    ER MODELING
    • Entity
    • An Entity is a conceptual object
    • Physically or conceptually exists
    • Usually a noun in requirement specification
      • E.g. Lecturer , Course , Movie , Sales-order
    Lecturer Course SSN Name Teach
    • Collection of Entities have same properties
    • An entity instance is a single occurrence of entity type
    • Described once in metadata
    • A noun in requirement specifications
    • A true data entity have many instances,each with distinguishing feature
    • A strong entity – exists independently like student , Course , Car
    • A weak entity – existence depends upon other entity (identifying owner)
    • Dependent
    ENTITY TYPES
    • Property , description of entities and entity types
    • Attribute type ( domain)
      • Define all possible attribute values
    • Attribute value , associated with individual entities
    • A noun or an objective in requirement specifications
    IM-99-02 Student ID DoB  Singh 4-9-77
    • Entities – Attributes
    • Entities have ‘independent’ meaning e.g. Car , Student
    • Attributes have no independent meaning
    ? ID ATTRIBUTE
    • Uniquely identify individual instances of an entity type
      • A key refers to one or a group of attributes as a whole
      • A key attribute refers to a component of a composite key
      • Definition of a key changes with Data Semantics
    • An entity type may have few keys
      • Primary key – One of candidate key
      • Secondary key – Other keys
    • The primary key attribute (s) underlined
    • Choose key – will not change
    • Choose key – Not Null
    • Avoid INTELLIGENT KEYS
    KEY ATTRIBUTE
    • Simple attribute
      • Can’t be broken into smaller values
      • Contains only atomic values
    • Composite attribute
      • Has component attribute
    • Single valued attribute
      • One only per attribute
    • Multi – Valued attribute
      • Contains multiple values
    ATTRIBUTE CLASSIFICATION
  • Student Roll No Skill Dob F.Name M.Name L.Name Name EXAMPLE Degree
    • It is an association among instances of one or more entities involved.
      • Label as
      • Verb in requirement specifications,in present tense & descriptive
      • Example
      • Model associations, not actions and process
    RELATIONSHIP Student Course Faculty Teach take advise
    • How is an entity linked to relationship ? [Participation]
    • How many relationship instances is an entity permitted to be linked to ? [cardinality]
    • Relationship instance is an association between entity instances, where each instance includes exactly one entity from each participating entity type
    JUSTIFICATION Student Advise Faculty       Akr Trupti Kris Ram Mohan Singh
  • E 1 E 2 R min,max min,max Participation 0 – Partial 1 - Total Cardinality 1 - - One M - - More than One O - - One Student CARDINALITY AND PARTICIPATION Advise Faculty X Z Y A B C D
  • Unary Relationship Binary Relationship Ternary/N-ary Relationship RELATIONSHIP DEGREES
  • Person Married (0,1) (0,1) Unary Relationship RELATIONSHIP DEGREE Employee Manager (0,m) (0,1)
  • One –to-one binary Relationship A A One –to-Many binary Relationship B C D X Y Z E1 E2 (1, 1) (0, m) BINARY RELATIONSHIP B C D X Y Z
  • Many to Many binary Relationship X Y Z A B C D BINARY RELATIONSHIP Justification E1 E2 R1 (0, m) (1, m)
  • Customer SELL Salesman Car TERNARY RELATIONSHIP CAR REP (0,1) (0, m) Sell Customer (0,M)
  • Unary n-ary to RELATIONSHIP DEGREE
    • Relationship attributes
    • Attributes describing relationship, like when ,where, what
    Faculty Student Advise Memo Time Date
    • A relationship instance must include all participants
    • Need to be careful when converted to binary relationship
    RELATIONSHIP ATTRIBUTES
    • Attributes on a relationship might suggest to convert it to entity , termed (---) as associative entity.
    Course EMP Completes Date m m CERT Date Number ASSOCIATIVE ENTITY
    • Conditions
      • Result entity independent meaning
      • Result entity participates in one or more other relationships
    Other Notations Mandatory One Mandatory Many Optional One Optional Many CONDITIONS AND NOTATIONS
  • BASIC E-R NOTATION
  • SAMPLE ER DIAGRAM
    • Conceptual Modeling – important skill
    • Conceptual Schema important design document – independent of DBMS
    • Entities , types
    • Relationships Unary – n-ary
    • Associations
      • Cardinality (Connectivity)
      • Participation (Degree)
    SUMMARY