Abstract Data Types (ADT) Overview

Study Notes

Abstract Data Type (ADT) Definition

An Abstract Data Type (ADT) is a specification for a data structure that defines its behavior but not its implementation.
It encapsulates data and operations on that data, hiding the underlying representation.
Users interact with an ADT through a well-defined interface, ignoring how the data is stored or manipulated internally.
This allows for flexibility in implementation choices without affecting the way the ADT is used.

Characteristics of ADTs

Abstraction: Hides the internal implementation details.
Encapsulation: Bundles data and operations that act on the data within a single unit.
Well-defined interface: Users interact with ADTs using specific operations (functions or methods).
Data and operations: Defines the types of data to be stored and the allowed operations on that data.
Implementation independence: The implementation can change without affecting user code.

Examples of ADTs

Stack:
- A data structure that stores elements in a last-in, first-out (LIFO) order.
- Key operations: push (add an element), pop (remove and return the top element), peek (return the top element without removing), isEmpty (check if empty), isFull (check if full).
Queue:
- A data structure that stores elements in a first-in, first-out (FIFO) order.
- Key operations: enqueue (add an element), dequeue (remove and return the front element), peek (return the front element without removing), isEmpty (check if empty), isFull (check if full).
List:
- A linear collection of elements, where elements can be accessed by their position.
- Key operations: insert, delete, access (using index), search.
Set:
- A collection of unique elements.
- Key operations: add, remove, contains, isEmpty, size.
Map/Dictionary:
- A collection of key-value pairs, where each key is unique.
- Key operations: put (add a key-value pair), get (return the value for a specific key), remove (remove a key-value pair), containsKey (check if a key exists).
Tree:
- A hierarchical data structure, typically with a root node and branches of nodes.
- Key operations: insert, delete, search, traverse (inorder, preorder, postorder).
Graph:
- A collection of nodes (vertices) connected by edges.
- Key operations: add node, add edge, traverse (depth-first search, breadth-first search).
Priority Queue:
- Queue where each element has an associated priority. The highest priority element is retrieved first.
- Key operations: insert, remove (retrieve the highest priority element), peek (get highest priority element without removal), isEmpty, size.
Circular Buffer/Queue:
- A buffer or queue whose last element is connected to the first element.
- Useful for simulating cyclic or continuous data streams.

Importance of ADTs

Modularity: Breaks down complex data structures into manageable components.
Maintainability: Easier to change or update the implementation without affecting the rest of the code.
Reusability: Can be used in different parts of a program or even in different programs without modification.
Abstraction: Handles the complexity of data structures.
Security: By hiding implementation details, prevents unauthorized access to internal data.

Description

This quiz covers the definition, characteristics, and examples of Abstract Data Types (ADT). You will learn about the importance of abstraction and encapsulation in data structures, as well as how users interact with ADTs through a well-defined interface. Test your understanding of these fundamental concepts in computer science.