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Conceptual Data Model

Learn about conceptual data modeling principles and various graphical languages to model data effectively. Explore Entity-Relationship Model (ERM), Class Diagram, Object Role Modeling (ORM), and more with examples and recommendations. Enhance your understanding of associations, attributes, cardinalities, and dimensions in data modeling concepts.

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Conceptual Data Model

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  1. Conceptual Data Model Principles Graphical Languages Modeling Constraints Bogdan Shishedjiev Conceptual Data Model

  2. Principles • Main approach – object-oriented • Class (entity set, object) • Association (relationship, relation) • Data member (attribute, property) • Instance (entity, occurrence) Bogdan Shishedjiev Conceptual Data Model

  3. Languages • Entity Relationship model (E-R) (ERM) • Entity set • Relationship • Attribute Bogdan Shishedjiev Conceptual Data Model

  4. Languages • MERISE • Object with occurrences • Relation • Propertiy Bogdan Shishedjiev Conceptual Data Model

  5. Languages • Object Role Modeling (ORM) Bogdan Shishedjiev Conceptual Data Model

  6. Languages • Class Diagram • Class with instances • Association • Property Bogdan Shishedjiev Conceptual Data Model

  7. Conceptual Model • Goals • Starting from the dictionary and the rules this model tries to reveal the relations among the data and their interaction • Example – School Rules • Every class has a one and only one room. • Every subject is teaches by only one teacher. • Every class is taught a subject a fixed number of hours. • Every student can have no more one mark in every subject. • The school manages the timetable and the rating of students and teachers.. Dictionary • Student’s Address, • Subject, • Number of Hours, • Class Name, • Student's Family Name, • Teacher‘s Name, • Mark, • Room Number, • Student’s Name Bogdan Shishedjiev Conceptual Data Model

  8. Concepts • Class (Entity class, Entity instance) • Association • Relationship between entity instances • Attribute • Properties Bogdan Shishedjiev Conceptual Data Model

  9. Attribute • Attribute NAME • First Name • Last Name • Address STUDENT Defining an Entity Class • Give it a name (a noun) • Define its attributes • Define the rules • What belongs to the class? • How the instances are identified in the class? • Identifying an instance (Identifier) Bogdan Shishedjiev Conceptual Data Model

  10. Association • Give a name (a verb) • Determine the participating classes • Define the cardinalities Bogdan Shishedjiev Conceptual Data Model

  11. Examples Bogdan Shishedjiev Conceptual Data Model

  12. Identifier of an Association Bogdan Shishedjiev Conceptual Data Model

  13. Cardinalities of an Association • Cardinalities One to One • 0..1 – 0..1 – Every student can use one locker • 0..1 – 1 • 1 – 1 Every student uses a locker and ther are no free lockers Bogdan Shishedjiev Conceptual Data Model

  14. Cardinalities of an Association • Cardinalities One to Many • 1 – 1..N • 0..1 – 1..N • 1 – 0..N • 0..1 – 0..N Bogdan Shishedjiev Conceptual Data Model

  15. Cardinalities of an Association • Cardinalities Many to Many • 1..N – 1..N • 0..N – 1..N • 0..N – 0..N Bogdan Shishedjiev Conceptual Data Model

  16. Cardinalities of an Association • Generalization • Minimal cardinality • Mandatory participation of every instance - 1 • Optionally participation of every instance - 0 • Maximal cardinality • To only one instance of the other class – 1 • To multiple instances of the other class - N Bogdan Shishedjiev Conceptual Data Model

  17. Dimension of an Association • Number of different classes participating in it • Multidimensional Bogdan Shishedjiev Conceptual Data Model

  18. Dimension of an Association • Multidimensional Bogdan Shishedjiev Conceptual Data Model

  19. Dimension of an Association • One-dimensional (Reflexive) Bogdan Shishedjiev Conceptual Data Model

  20. Aggregate Associations • Aggregation • Composition Bogdan Shishedjiev Conceptual Data Model

  21. Weak Entities • It is identified through the association Bogdan Shishedjiev Conceptual Data Model

  22. Recommendations • Don’t use high dimension associations • Be aware not replace classes by associations Bogdan Shishedjiev Conceptual Data Model

  23. A patient is characterized by: Unique Number Name address Phone Number General practitioner is characterized by: Serial Number Name Phone Number Each patient is supervised by a GP A policlinic is characterized by: Name Address Phone Number A specialist is characterized by: Serial Number Name Phone Number Each specialist has one or more specialties Each specialist can give consultations in one or more policlinics Each policlinic groups several specialists A patient can make an appointment for a consultation with specialist in a given policlinic, The specialist must work in this policlinic The appointment is for a date that is later than the date of appointment If the consultation does not take place a new appointment must be made no matter what are reasons for the failure Lists of appointment for every specialist are made at the beginning of the day. In the end of every day two reports are made: A log of appointment made A log of consultations done Case Study – Management Rules Bogdan Shishedjiev Conceptual Data Model

  24. Case Study - Policlinic Bogdan Shishedjiev Conceptual Data Model

  25. Case Study - Policlinic Bogdan Shishedjiev Conceptual Data Model

  26. Subtypes • Example – Hardware components order Bogdan Shishedjiev Conceptual Data Model

  27. Subtypes Bogdan Shishedjiev Conceptual Data Model

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