Conceptual Modeling and Entity-Relationship Diagrams - PowerPoint PPT Presentation

conceptual modeling and entity relationship diagrams n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Conceptual Modeling and Entity-Relationship Diagrams PowerPoint Presentation
Download Presentation
Conceptual Modeling and Entity-Relationship Diagrams

play fullscreen
1 / 95
Conceptual Modeling and Entity-Relationship Diagrams
239 Views
Download Presentation
lyle
Download Presentation

Conceptual Modeling and Entity-Relationship Diagrams

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Conceptual Modeling andEntity-Relationship Diagrams Chapter 3: Elmasri/Navathe

  2. Outline • Phases of Database Design • Conceptual Modeling • Abstractions in Conceptual Design • Example Database Requirements • Deconstructing the E-R Diagram • Entities, Attributes and Relationships • Participation, Cardinality and Keys 3753 X2 - 2004

  3. Phases of Database Design Application Domain Requirements Analysis Database Requirements DBMS Independent Conceptual Design Conceptual Schema Data Model Mapping Implementation Schema Physical Design DBMS Dependent Physical Schema 3753 X2 - 2004

  4. A Data Modeling Process • Steps in the data modeling process • Plan project • Determine requirements • Specify entities • Specify relationships • Determine identifiers • Specify attributes • Specify domains • Validate model 3753 X2 - 2004

  5. Planning the Project • Obtaining project authorization and budget • Building the project team • Planning the team’s activities • Establishing tools, techniques, and standards for consistent results • Defining the project’s scope 3753 X2 - 2004

  6. Determining System Requirements • Sources for data modeling requirements • User interviews and user activity observations • Existing forms and reports • New forms and reports • Existing manual files • Existing computer files/databases • Formally defined interfaces (XML) • Domain expertise • The result of the requirements determination will be a repository of notes, diagram, forms reports, files, etc., that can be used to develop the data model 3753 X2 - 2004

  7. Specifying Entities • An entity is something that the users want to track; something the users want to keep data about • Entities • can be physical things or logical concepts • are identifiable; you can tell one from another • are things described by nouns, not characteristics described by adjectives 3753 X2 - 2004

  8. Specifying Relationships • Includes: • Identity of the parent and child entities • Relationship type • Minimum and maximum cardinalities • Name of the relationships • Two techniques: • Examine whether a relationship exists between every combination of two entities • Locate relationships from requirement documents • A combination of the two approaches may be used 3753 X2 - 2004

  9. Determining Identifiers • Identifier is an attribute or group of attributes that uniquely identifies an entity instance • If there is difficulty specifying an identifier, maybe: • it should be part of a different entity • it is a subtype or category of a common entity • it needs one or more identifying relationships 3753 X2 - 2004

  10. Specifying Attributes and Domains • Find attributes on forms, reports, existing files, etc., and add them to entities • Determine whether the attribute has already defined a domain • If so, the attribute is based upon that domain • If not, a new domain is defined • Review the domains and make adjustments as necessary • Domain property inheritance: when the domain properties change, all the attribute properties change as well • Domains may be used to enforce data standards promoting compatible data types and systems • Once all attributes have been specified the model should be reviewed for missing entities 3753 X2 - 2004

  11. Validating Model • Data model is a model of humans’ models, not a model of reality • A data model is wrong if it does not accurately reflect the ways the users think about their world • Data models are validated through a series of reviews • Normally, a team review is followed by user reviews • E-R model as well as prototypes of forms and reports may be used to communicate to users features of the data model 3753 X2 - 2004

  12. Creating Data Models From Forms and Reports • Example: Single entities 3753 X2 - 2004

  13. Example: Identifying Connection Relationships 3753 X2 - 2004

  14. Example: Repeating Groups 3753 X2 - 2004

  15. Example: Repeating Groups 3753 X2 - 2004

  16. Example: Nested Groups 3753 X2 - 2004

  17. Example: Non-Identifying Connection Relationships • Example: 1:1 3753 X2 - 2004

  18. Example: Non-Identifying Connection Relationships 3753 X2 - 2004

  19. Example: 1:N 3753 X2 - 2004

  20. Example: 1:N 3753 X2 - 2004

  21. Example: 1:N 3753 X2 - 2004

  22. Example: N:M 3753 X2 - 2004

  23. Example: Assignment Relationship 3753 X2 - 2004

  24. Example: Assignment Relationship 3753 X2 - 2004

  25. Example: Category Relationship 3753 X2 - 2004

  26. Example: Category Relationship 3753 X2 - 2004

  27. Example: Category Relationship 3753 X2 - 2004

  28. Sales-Order Model 3753 X2 - 2004

  29. Example: Sales Order 3753 X2 - 2004

  30. Example: Sales Order • Figure 3-16(c) shows an alternative design that allows an item to appear more than once on a given order 3753 X2 - 2004

  31. Conceptual Design • Similar to the analysis phase in software development • produce a description of the data • capture the semantics of the data • Description in a high-level model • close to the user’s view of the world • abstract concepts • means of communication between the user and the developer 3753 X2 - 2004

  32. Reasons for Conceptual Modeling • Independent of DBMS. • Allows for easy communication between end-users and developers. • Has a clear method to convert from high-level model to relational model. • Conceptual schema is a permanent description of the database requirements. 3753 X2 - 2004

  33. Abstractions in Conceptual Design • An abstraction is a mental process where we select some set of properties of an object and exclude others. • 3 types of abstractions • classification • aggregation • generalization 3753 X2 - 2004

  34. Classification • Define a class of real-world objects with common properties Month … January February December 3753 X2 - 2004

  35. Aggregation • Define a new class from a set of other classes that represent component parts Car Tires Steering Wheel Engine Gas pedal 3753 X2 - 2004

  36. Generalization • Defines a subset relationship between elements of 2 or more classes Person Employee Student Faculty Staff 3753 X2 - 2004

  37. Entity-Relationship Model • Most popular conceptual model for database design • Basis for many other models • Describes the data in a system and how that data is related • Describes data as entities, attributes and relationships 3753 X2 - 2004

  38. Database requirements • We must convert the written database requirements into an E-R diagram • Need to determine the entities, attributes and relationships. • nouns = entities • adjectives = attributes • verbs = relationships 3753 X2 - 2004

  39. Academic Teaching Database Design an E-R schema for a database to store info about professors, courses and course sections indicating the following: • The name and employee ID number of each professor • The salary and email address(es) for each professor • How long each professor has been at the university • The course sections each professor teaches • The name, number and topic for each course offered • The section and room number for each course section • Each course section must have only one professor • Each course can have multiple sections 3753 X2 - 2004

  40. Visual View of the Database Employee ID Years Teaching Section ID Room Start Date N 1 Section Professor teaches Email N Salary First Part of Name Last 1 Number Course Topic Name

  41. The Pieces • Objects • Entity (including weak entities) • Attribute • Relationship • “Structural” Constraints • Cardinality • Participation 3753 X2 - 2004

  42. Entities • Entity – basic object of the E-R model • Represents a “thing” with an independent existence • Can exist physically or conceptually • a professor, a student, a course • Entity type – used to define a set of entities with the same properties. 3753 X2 - 2004

  43. Number Name Topic Entity Type Course Entity and Entity Types Number: 3753 Name: Database Management Systems Topic: Introduction to DBMSs Entity 3753 X2 - 2004

  44. Attributes • Each entity has a set of associated properties that describes the entity. These properties are known as attributes. • Attributes can be: • Simple or Composite • Single or Multi-valued • Stored or Derived • NULL 3753 X2 - 2004

  45. First Last Professor Composite Name Attributes (cont’d) Simple Professor Start Date 3753 X2 - 2004

  46. Multi-Valued Professor Email Attributes (cont’d) Single Professor Employee ID# 3753 X2 - 2004

  47. Derived Professor Years Teaching Attributes (cont’d) Stored Professor Start Date 3753 X2 - 2004

  48. Attributes (cont’d) • NULL attributes have no value • not 0 (zero) • not a blank string • Attributes can be “nullable” where a null value is allowed, or “not nullable” where they must have a value. 3753 X2 - 2004

  49. Primary Keys • Employee ID is the primary key • Primary keys must be unique for the entity in question Professor Employee ID 3753 X2 - 2004

  50. Relationships • defines a set of associations between various entities • can have attributes to define them • are limited by: • Participation • Cardinality Ratio 3753 X2 - 2004