Advanced Data Representation

Jul 12, 2024

Review flashcards

Advanced A-Level Computer Science

Chapter 16: Advanced Data Representation

Overview

  • Topics Covered: Data Types, File Storage & Access, Binary Representation of Real Numbers.
  • Focus: Understanding advanced concepts of data representation for A-Level Computer Science.

Data Types

  • Definition: Specifies the type of data that a variable can store. It's crucial for accurate data interpretation by computers.
  • Examples: ASCII for characters, Binary for denary numbers.

Built-in vs. User-defined Data Types

  • Built-in Data Types: Predefined by programming languages. E.g., Integer, Floating Point, Character, Boolean, etc.
    • Features: Range of values, supported operations.
    • Example (Java): Integer data type has predefined range and operations.
  • User-defined Data Types: Defined by users. E.g., Classes in Python.
    • Composite Data Types: Arrays, Lists, Dictionaries – contain multiple values.
    • Non-composite Data Types: Single values with no subcomponents.
    • Example (Python): Creating a class for 'Point', representing coordinates.

Enumerated Data Types

  • A collection of named constant values representing a set of related items/choices.
  • Examples: Days of the week, RGB colors.

Pointer Data Types

  • Store memory addresses of other variables/data elements.
  • Example: Implementing pointer types to access memory locations directly.

Set Data Types

  • Unordered collection of unique elements (no duplicates).
  • Features: Unique elements, unstructured, no indexing.
  • Operations: Check value existence, add/remove elements, set intersections.

File Organization

  • Types of Files: Text files, Binary files.
    • Text Files: Human-readable (e.g., .txt files).
    • Binary Files: Computer-readable, sequence of bytes.

Record Concept in File Organization

  • Record: Basic unit of data storage within a file.
  • Example: Image files (pixels), Audio files (amplitude over time).

Types of File Organization

  1. Serial File: Records are stored one after the other.
    • Use Case: Daily transaction logs in financial institutions.
  2. Sequential File: Records stored and accessed in a predefined order.
    • Use Case: Order processing.
  3. Direct Access File: Records accessed directly without traversing the entire file.
    • Methods: Index files, Hashing algorithms.

File Access and Deletion

  • Serial and Sequential Files: Traverse from start to end, or key value access for sequential files.
  • Direct Access Files: Quick access via indexing or hashing.
  • Deletion: Marking records for deletion rather than immediate removal.

Binary Representation of Real Numbers

  • Fixed Point Representation: Predefined number of bits for fractional and integer parts.
    • Example: 4 bits integer, 3 bits fractional, results from limited precision.
  • Floating Point Representation: Flexible, representing larger ranges of numbers.
    • Components: Mantisa, Exponent, Base.
    • Example: Assign bits to mantisa and exponent, normalize for precision.

Conversion and Normalization

  • Steps for Conversion: Whole number conversion, fractional conversion, normalize mantisa.
  • Examples: Converting 9.7 and 9.73 to binary representation.
  • Normalization: Adjusting exponent and mantisa for maximum precision.
    • Ensures the two most significant bits are different.

Problems with Floating Point Numbers

  • Approximation and Rounding Errors: Due to limited precision.
  • Limited Range of Numbers: Requires more bits for more precision.

Conclusion

  • Review: Advanced data representation is complex but crucial for accurate computation and storage.
  • Next Steps: Watch extra tutorials for clarity on complex topics.

Thank you for watching!