Data structures can be classified into various taxonomies based on their characteristics, properties, and usage. Here are some common taxonomies for data structures:
- Primitive Data Structures:
- These are the basic building blocks provided by programming languages, such as integers, floating-point numbers, characters, and boolean values. They are not further divisible within the programming language.
- Linear Data Structures:
- Data elements are arranged sequentially, and each element has a unique predecessor and successor, except for the first and last elements. Examples include arrays, linked lists, stacks, and queues.
- Non-linear Data Structures:
- Elements may have multiple predecessors and successors, forming a hierarchical or interconnected structure. Examples include trees and graphs.
- Homogeneous Data Structures:
- All elements in the structure are of the same data type. Arrays are an example of homogeneous data structures.
- Heterogeneous Data Structures:
- Elements in the structure can be of different data types. Structures (or records) and tuples are examples of heterogeneous data structures.
- Static Data Structures:
- The size of the data structure is fixed at compile-time and cannot be changed during runtime. Arrays are an example of a static data structure.
- Dynamic Data Structures:
- The size of the data structure can change during program execution. Examples include linked lists and dynamic arrays.
- Primitive Recursive Data Structures:
- Data structures that can be easily traversed using recursion. Examples include linear data structures like linked lists.
- Composite Data Structures:
- These are structures that are composed of multiple primitive or composite data structures. Examples include arrays of records, trees of arrays, etc.
- Abstract Data Types (ADTs):
- ADTs define a set of operations on data without specifying the implementation details. Examples include stacks, queues, and hash tables.
- Persistent Data Structures:
- Structures that allow for efficient versioning and retrieval of previous states even after modifications. These are particularly useful in scenarios where you need to maintain a historical record of changes.
- Categorization Based on Operations:
- Data structures can be categorized based on the operations they support, such as searching, sorting, insertion, deletion, etc.
- Specialized Data Structures:
- These are structures designed to solve specific types of problems efficiently. Examples include heaps, hash tables, and bloom filters.
- Spatial Data Structures:
- Designed for storing and querying spatial data efficiently, such as quad trees and octrees in computer graphics or GIS applications.
- External Data Structures:
- Optimized for managing data that does not fit entirely in memory, typically used in scenarios involving large datasets stored on external storage devices.
Understanding these taxonomies helps developers choose the most appropriate data structure for a specific problem, considering factors like time complexity, space complexity, and the type of operations required.
‘Data Structures Taxonomies‘