Visualization Tool for Data Structures in Real Time

Authors

  • Dr. Linda Uchenna Oghenekaro

Keywords:

Mobile application, Instagram, Google meet and Speaking accuracy, Integration, Artificial Intelligence (AI), Clinical diagnosis, healthcare professionals, complexity, Data Structures, Visualization, Hybrid Input, object-oriented methodology

Abstract

Data structures are crucial aspects of Computer Science, but grasping their abstract nature poses challenges for students and software developers. A visualization tool for data structures such as arrays, stacks, and trees would effectively simplify the complex nature of data structures and algorithms. This research utilized an object-oriented approach to create a real-time Visualization Tool for Data Structures. This tool offers visual representations of fundamental data structures such as arrays and trees. The visualization tool is web-based and developed with HTML, CSS, and JavaScript technologies. The tool’s efficacy underwent evaluation using complexity metrics. Results notably demonstrate that as the volume of data increases, the complexity of data structures follows suit. Consequently, this paper serves as an informative resource concerning the selection of data types and their respective implementation styles within data structures. Such insights furnish developers with valuable knowledge regarding the efficiency of diverse data types in software development, empowering informed decisions when choosing between data types based on their impact on space complexities within data structures.

References

E. Vrachnos, A. Jimoyiannis (2014) Design and evaluation of a web-based dynamic algorithm visualization environment for novices. 27, 229-239.

A.F. Blanco, A. Bergel, J.P.S. Alcocer (2022) Software visualizations to analyze memory consumption: A literature review. 55(1), 1-34.

D. Burlinson, M. Mehedint, C. Grafer, K. Subramanian, J. Payton, P. Goolkasian, M. Youngblood, R. Kosara (2016) BRIDGES: A system to enable creation of engaging data structures assignments with real-world data and visualizations. 18-23.

R.A. Nathasya, O. Karnalim, M. Ayub (2019) Integrating program and algorithm visualisation for learning data structure implementation. 20(3), 193-204.

J. Akram, L. Fang (2015) Cognitive effects of visualization on learning data structure and algorithms. 70.

A.N.F. Ab Rahman, N. Khalid, F. Abdullah (2016) Web-Based Visualization Tools Of Data Structure & Algorithm-A Review Of Experience.

K. Romanowska, G. Singh, M.A.A. Dewan, F. Lin (2018) Towards developing an effective algorithm visualization tool for online learning. 2011-2016.

V. Lazaridis, N. Samaras, A. Sifaleras (2013) An empirical study on factors influencing the effectiveness of algorithm visualization. 21(3), 410-420.

A.A. Supli, N. Shiratuddin, S.B. Zaibon (2016) Critical analysis on algorithm visualization study.

S. Buchanan, B. Ochs, J.J. LaViola Jr (2012) CSTutor: a pen-based tutor for data structure visualization. 565-570.

D. Galles Data Structure Visualizations. http://www.cs.usfca.edu/~galles/visualization/Algorithms.html

S. Šimoňák, M. Benej (2014) Visualizing algorithms and data structures using the algomaster platform. 12(2), 189-201.

A. Kumari, M. Mittal, V. Jha, A. Sahu, M. Kumar, N. Sangwan, N. Bohra (2022) Algorithm Visualization-Modern Web-Based Visualization of Sorting and Searching Algorithms. 21(5), 2721-2736.

M. Mukherjee (2016) Object-Oriented Analysis and Design. 1(1), 1-11.

B. Ma, S.K. Suter, A. Entezari (2017) Quality assessment of volume compression approaches using isovalue clustering. 63, 18-27.

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Published

2023-12-30

How to Cite

Visualization Tool for Data Structures in Real Time. (2023). London Journal of Research In Computer Science and Technology, 23(5), 33-44. https://journalspress.uk/index.php/LJRCST/article/view/863