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DATA STRUCTURES AND ALGORITHMS

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Bibliographic Details
Title: DATA STRUCTURES AND ALGORITHMS
Authors: Monu Sharma, Amit Dhiman, Ms. Nitya, Mr Shubneet, Anushka Raj Yadav
Source: Intelligent Shields: Artificial Intelligence and Machine Learning for Cybersecurity ISBN: 9789370208926
Publisher Information: Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025.
Publication Year: 2025
Description: Data structures and algorithms serve as the cornerstone of efficient computational problemsolving, enabling the organization and manipulation of data with optimal resource utilization. This chapter delves into foundational data structures, including arrays, linked lists, stacks, queues, and trees, alongside critical algorithms for sorting (e.g., quicksort, bubble sort) and searching (e.g., binary search). Theoretical principles such as time/space complexity analysis and memory management are examined to underscore their impact on algorithmic efficiency [1, 2]. Practical applications—such as hash tables for database indexing and graph algorithms for social network analysis—illustrate the real-world relevance of these concepts [3, 4]. Visual aids, including memory diagrams for linked lists and recursion call stacks, clarify intricate operations and enhance conceptual understanding. By bridging abstract data types with concrete implementations, the chapter equips readers to design scalable solutions for complex computational challenges.
Document Type: Part of book or chapter of book
DOI: 10.58532/nbennurasai6
Accession Number: edsair.doi...........e1f79330b356da31bb0a12c64c0cf9a8
Database: OpenAIRE
Description
Abstract:Data structures and algorithms serve as the cornerstone of efficient computational problemsolving, enabling the organization and manipulation of data with optimal resource utilization. This chapter delves into foundational data structures, including arrays, linked lists, stacks, queues, and trees, alongside critical algorithms for sorting (e.g., quicksort, bubble sort) and searching (e.g., binary search). Theoretical principles such as time/space complexity analysis and memory management are examined to underscore their impact on algorithmic efficiency [1, 2]. Practical applications—such as hash tables for database indexing and graph algorithms for social network analysis—illustrate the real-world relevance of these concepts [3, 4]. Visual aids, including memory diagrams for linked lists and recursion call stacks, clarify intricate operations and enhance conceptual understanding. By bridging abstract data types with concrete implementations, the chapter equips readers to design scalable solutions for complex computational challenges.
DOI:10.58532/nbennurasai6