APPLICATIONS of DATA STRUCTURES in DATA COMPRESSION and MEMORY MANAGEMENT

 ✅ Applications in Data Compression

Data compression involves reducing the size of data without losing essential information. Efficient data structures help achieve this by optimizing how data is stored and processed.

1. Huffman Coding (Trees)

• Data Structure Used: Binary Trees / Priority Queues (Min-Heap)

• Application: Lossless compression technique used in ZIP files, JPEG, MP3.

• How it works:

  • Frequencies of symbols are stored in a priority queue.

  • A binary tree is built where frequently used characters are closer to the root, giving them shorter binary codes.

2. Tries (Prefix Trees)

• Application: Used in dictionary-based compression algorithms like LZ78, LZW.

• How it works:

  • Stores common prefixes efficiently.

  • Helps avoid storing redundant data by referencing previously seen patterns.

3. Run-Length Encoding (RLE)

• Data Structure Used: Arrays / Lists

• Application: Useful for compressing sequences with repeated elements (e.g., simple images).

• How it works:

  • Consecutive duplicates are stored as a single value and a count.

4. Burrows-Wheeler Transform (BWT)

• Data Structure Used: Arrays, Strings, Suffix Arrays

• Application: Used in bzip2 and other modern compressors.

• How it works:

  • Rearranges data to increase the locality of reference, making it more compressible by RLE or Huffman coding.

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✅ Applications in Memory Management

Efficient memory management ensures optimal use of system memory, reducing waste and speeding up program execution.

1. Stacks

• Application: Function call management (call stack), memory allocation for recursion.

• How it works:

  • Last-In-First-Out (LIFO) structure that helps manage execution context during function calls and returns.

2. Heaps

• Application: Dynamic memory allocation (e.g., malloc in C uses heap).

• How it works:

  • Memory blocks are dynamically allocated from the heap region of memory, managed using free lists or tree-based structures.

3. Linked Lists

• Application: Managing free memory blocks (free list).

• How it works:

  • Memory is tracked using linked nodes, enabling efficient insertion/deletion without moving memory.

4. Buddy Memory Allocation (Binary Trees)

• Application: Dynamic memory allocation in operating systems.

• How it works:

  • Memory is split into halves recursively (like a binary tree).

  • Merges free blocks to reduce fragmentation.

5. Bitmaps

• Application: Tracking memory usage.

• How it works:

  • Each bit in a bitmap represents a block of memory (0 = free, 1 = used).

  • Efficient for quick checking of memory availability.

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Summary Table

Area	Data Structure	Application Example
Data Compression	Binary Tree	Huffman Coding
	Trie	LZ78, LZW
	Arrays	RLE, BWT
	Min-Heap	Prioritizing symbols in Huffman
Memory Management	Stack	Function calls, recursion
	Heap	Dynamic memory allocation
	Linked List	Free list in allocators
	Binary Tree	Buddy allocator
	Bitmap	Memory tracking

APPLICATIONS of DATA STRUCTURES in DATA COMPRESSION and MEMORY MANAGEMENT

• Data compression: Data compression is the process of reducing the size of data without losing any important information. Data structures can be used to compress data by identifying and removing redundant or unnecessary information. For example, a hash table can be used to compress a text file by storing the frequency of each word in the file. This information can then be used to create a compressed version of the file that only stores the unique words and their frequencies.
• Memory management: Memory management is the process of allocating and freeing memory resources in a computer system. Data structures can be used to manage memory by organizing data in a way that makes it easy to find and access. For example, a linked list can be used to manage memory by storing pointers to free memory blocks. This information can then be used to quickly find a free memory block when it is needed.

Examples of how data structures are used in data compression and memory management:

• Huffman coding: Huffman coding is a lossless data compression algorithm that uses a binary tree to represent the frequencies of symbols in a data stream. The tree is then used to encode the data stream, resulting in a compressed version of the data that is smaller than the original.
• LZW compression: LZW compression is a lossless data compression algorithm that uses a dictionary to represent the symbols in a data stream. The dictionary is then used to encode the data stream, resulting in a compressed version of the data that is smaller than the original.
• Linked lists: Linked lists are often used to manage memory in a computer system. This is because linked lists can be easily extended and modified, which makes them well-suited for representing dynamic data structures.
• Hash tables: Hash tables are often used to manage memory in a computer system. This is because hash tables can be used to quickly find a free memory block when it is needed.