Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

Navigating Data Streams Using Advanced Data Analysis And Visualization Techniques

Authors
Chengamma Chitteti1, *, Thotanipalli Hetheesh3, K. Reddy Madhavi2, Naru Pavan Kumar4, Suresh Telagathoti3, Putakala Naga Venkata Rakesh3, Shiva Kaleru4
1Dept.of CSE (DS), Sree Vidyanikethan Engineering College, Tirupati, India
2Dept.of AI&ML, Sree Vidyanikethan Engineering College, Tirupati, India
3UG Scholar, Dept.of IT, Sree Vidyanikethan Engineering College, Tirupati, India
4Juniper Networks, Sunnyvale, USA
*Corresponding author. Email: sailusrav@gmail.com
Corresponding Author
Chengamma Chitteti
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_25How to use a DOI?
Keywords
Big data; Data streams; Data analysis; Visualization methods; Advanced analysis; Machine learning algorithms; Framework
Abstract

Big data has not only exacerbated but in some cases created unimaginable challenges for enterprises in terms of getting a grip of the tidal wave of data sources that are monstrously big. The main aim of this research is to design and implement a technology that exploits modern advanced data analysis and visualization methods to navigate through the flow of rapidly shifting and fast-running data streams. The first part of the review of the study concludes that the comprehension of the flow of real-time data stream is too complex and, therefore, the need for sophisticated analysis methods. We address integration of modern data analysis techniques, such as machine learning algorithms, anomaly detection or predictive modelling is used to analyze streaming impact decisively. Besides, the paper proves the point that visualization breaks down the complexity of the patterns in data and makes such patterns understandable. We present interaction and immersive visualization tool portrayals of real time complex data streams and assists the decision makers to understand them. We seek to forge the link between raw data and meaningful information by blending smart analytics with informative graphical illustrations. The paper finally draws the readers’ attention to various case studies and real-world examples that are related to several areas to demonstrate that the method above works in resolving some problems in those areas. We will take a look at how these strategies help in optimizing operation, resource allocation as well as fleet management besides being able to adjust quickly in response to new trends. By the end this study serves as an indispensable reference for the data analytics field in which the framework of data flow management is systematically designed. It shows the diversity and variety of solutions which gives a significant opportunity to businesses to get useful insights from the ongoing data streams that are changing all the time, assisting them to take prompt and well-grounded decisions in the fast progressive age when information is important.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_25How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Chengamma Chitteti
AU  - Thotanipalli Hetheesh
AU  - K. Reddy Madhavi
AU  - Naru Pavan Kumar
AU  - Suresh Telagathoti
AU  - Putakala Naga Venkata Rakesh
AU  - Shiva Kaleru
PY  - 2024
DA  - 2024/07/30
TI  - Navigating Data Streams Using Advanced Data Analysis And Visualization Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 253
EP  - 263
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_25
DO  - 10.2991/978-94-6463-471-6_25
ID  - Chitteti2024
ER  -