Utilizing Machine Learning in Big Data Visualization: A Systematic Review

Ade Kurniawan, Aldi Rosyid, Ade Rudi Masa’id, Wendi Usino

Abstract


This systematic review examines the existing literature on data visualization approaches for big data, aiming to identify key challenges, emerging trends, and promising solutions. The review highlights the growing importance of data visualization in extracting meaningful insights from large and complex datasets. It underscores the unique challenges posed by the scale and multidimensionality of big data, emphasizing the need for innovative visualization techniques. The paper explores various data visualization methods currently employed in big data analysis, discussing their strengths, limitations, and potential areas for future development. The review concludes by emphasizing the importance of continued research in this field to improve the accessibility and effectiveness of big data visualization for a wide range of applications.  

Keywords


Data Visualization, Automation, Machine Learning, Data Visualization Machine Learning

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References


S. Mysore, M. Jasim, H. Song, S. Akbar, A. K. C. Randall and N. Mahyar, "How Data Scientists Review the Scholarly Literature".

S. M. Ali, N. Gupta, G. K. Nayak and R. K. Lenka, "Big data visualization: Tools and challenges".

G. Chawla, S. Bamal and R. Khatana, "Big Data Analytics for Data Visualization: Review of Techniques".

E. Y. Gorodov and V. Gubarev, "Analytical Review of Data Visualization Methods in Application to Big Data".

D. Tranfield, D. Denyer, and P. Smart, ‘Towards a methodology for developing evidence‐informed management knowledge by means of systematic review’, British journal of management, vol. 14, no. 3, pp. 207–222, 2003.

M. J. Page et al., ‘The PRISMA 2020 statement: an updated guideline for reporting systematic reviews’, International journal of surgery, vol. 88, p. 105906, 2021.

Y. Wang, H. Huang, C. Rudin, and Y. Shaposhnik, "Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization," Journal of Machine Learning Research, vol. 22, pp. 1-73, 2021.​

M. H. Allaymoun, M. Khaled, F. Saleh, and F. Merza, "Data Visualization and Statistical Graphics in Big Data Analysis by Google Data Studio – Sales Case Study," 2022 IEEE Technology and Engineering Management Conference, 2022, pp. X-X.​

D. Shi, A. Oulasvirta, T. Weinkauf and N. Cao, "Understanding and Automating Graphical Annotations on Animated Scatterplots," IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 821-830, Jan. 2019. doi: 10.1109/TVCG.2018.2865000​

M. Ali, J. Choudhary, and T. Kasbe, "A hybrid model for data visualization using linear algebra methods and machine learning algorithm," Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 1, pp. 463-475, Jan. 2024. doi: 10.11591/ijeecs.v33.i1.pp463-475​

Y. Qiu and J. Lu, “A visualization algorithm for medical big data based on deep learning,” Measurement, vol. 183, p. 109808, 2021.​

G. Yan and B. Yu, "An Intelligent Visualization Method for Classic and Famous Prescriptions Based on Big Data," 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics, Dhaka, Bangladesh, 2024, pp. 1-6.​




DOI: http://dx.doi.org/10.36448/jsit.v17i1.3818

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Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika)
e-ISSN: 2686-181X
Website: http://jurnal.ubl.ac.id/index.php/explore
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