Produce An Entity-Relationship Model For The Scenario Descri ✓ Solved

Produce an Entity-Relationship Model for the scenario described above (15 marks)

Develop a top-down design of the data in the form of an entity-relationship diagram. You should note all assumptions you make about the data and the reasoning behind your design choices. Also include any appropriate constraints and a list of entity types showing their attributes and identifiers.

Sample Paper For Above instruction

Title: Designing a comprehensive Entity-Relationship Model for ATS animal tracking and help desk system

Introduction

In the realm of wildlife conservation, veterinary care, law enforcement, and private pet management, the collection and management of data related to animal tracking and support systems are paramount. ATS (Animal Tracking Systems) provides diverse functionalities ranging from real-time location monitoring to health and behavioral data analysis. The complexity of these operations necessitates a carefully designed database that can efficiently store, retrieve, and analyze data while supporting operational workflows and management reporting. This paper develops an Entity-Relationship (E-R) model capturing key data entities, their attributes, and inter-relationships, based on the provided scenario with justified assumptions and design considerations.

Assumptions and Design Rationale

Given the description, several assumptions underpin the E-R model:

  • Each Animal is uniquely identified by a Chip ID, with attributes such as species, age, and owner details.
  • Software products are distinguished by a unique Product ID, with attributes including name, version, and functionality description.
  • Help Desk Calls are tracked with unique Call IDs, including date and time, status, and resolution details.
  • Consultants are skilled on specific software products. A many-to-many relationship exists between Consultants and Software Products.
  • Call Reports are linked to specific Calls and contain details like problem description, status, and assigned consultant(s).
  • Calls can be transferred among consultants; thus, a relationship captures transfer history or current assignment.
  • Clients may be individuals or organizations, identified by Client IDs, with contact info stored.

Entity Types and Attributes

  1. Animal
    • AnimalID (PK)
    • Species
    • Age
    • OwnerID (FK to Client)
    • TrackingData
  2. Client
    • ClientID (PK)
    • Name
    • Address
    • ContactNumber
  3. SoftwareProduct
    • ProductID (PK)
    • Name
    • Version
    • Description
  4. HelpDeskCall
    • CallID (PK)
    • CallDateTime
    • CallerID (FK to Client)
    • SoftwareInvolved (FK to SoftwareProduct)
    • Status (e.g., open, in-progress, closed)
    • CallType (help desk vs other)
  5. CallReport
    • ReportID (PK)
    • CallID (FK to HelpDeskCall)
    • ProblemDescription
    • Resolution
    • ReportStatus (open, resolved, ongoing)
    • AssignedConsultantID (FK to Consultant)
  6. Consultant
    • ConsultantID (PK)
    • Name
    • Skills (related to software)

Relationships and Constraints

The entity relationships include:

  • Animal to Client: Many animals belong to one owner (many-to-one)
  • HelpDeskCall to Client: Many calls associated with one client
  • HelpDeskCall to SoftwareProduct: Many calls involve one software product
  • CallReport to HelpDeskCall: One to one or one to many, depending on whether multiple reports per call are allowed
  • CallReport to Consultant: Many reports can be assigned to one consultant; a call report can be transferred among consultants, implying a many-to-many relationship if needed
  • Consultant to SoftwareProduct: Many-to-many, as a consultant can be skilled in multiple products, and products can have multiple consultants

The primary keys are indicated, and foreign key constraints enforce referential integrity. Additional constraints include optional fields (such as resolution details) and status enumerations.

Conclusion

The proposed ER model encapsulates the core data entities, their attributes, and inter-relationships essential for supporting ATS operational and reporting needs. Assumptions were clearly documented to address gaps in the scenario description. This model forms a solid foundation for subsequent normalization, physical database design, and implementation steps.

References

  • Fundamentals of Database Systems (7th ed.). Pearson.
  • Database Design and Relational Theory. O'Reilly Media.
  • Database System Concepts (7th ed.). McGraw-Hill Education.
  • Relational Database Design Clearly Explained. Morgan Kaufmann.
  • Physical Database Design: The Database Result Set (4th ed.). Morgan Kaufmann.
  • Database Systems: Design, Implementation, and Management (9th ed.). Cengage Learning.
  • A First Course in Database Systems. Prentice Hall.
  • Relational database design. Elsevier.
  • Database Management Systems. McGraw-Hill.
  • Database Concepts. Pearson.

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