Integrating Big Data Management with Machine Learning in Cloud Environments

Title

Integrating Big Data Management with Machine Learning in Cloud Environments

Creator

Dabbir, Rupesh; Logeshwaran, J.

Description

In past years, the consideration of cloud environments for big data management and machine learning techniques has increased exponentially. However, the massive amount of data that is made by companies and Internet of Things (IoT) devices has presented the industry with a challenge in storage. In addition, conventional data management methods are unable to manage the complexity, diversity, and volume of this big data. Consequently, cloud environments integrate big data management and machine learning techniques. Combining big data management through cloud environments equipped with machine learning has multiple benefits. First, it is an efficient way to process and analyze large datasets in a distributed and scalable manner. Second, it helps businesses to provide insights and make time to make datadriven decisions. Thirdly, it enables the system to learn from data on an ongoing basis, which helps improve the quality and accuracy of the data. Now, this integration also enhances the overall performance of cloud systems as data management tasks are automated, minimizing manual efforts. Utilizing Big Data Management and Machine Learning Techniques in Cloud Environment to Address the Drawbacks of Conventional Data Management Approaches and Exploit the Value of Big Data These two technologies, when integrated, will allow the enterprise to manage significant amounts of data and derive important insights to arrive at better decisions. Cloud environments provide the automation and scalability required to push this integration further and change considerable data use in businesses. 2025 IEEE.

Source

2025 International Conference on Networks and Cryptology, NETCRYPT 2025;pp.443-447

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Automation; Cloud Systems; Complexity; Conventional Data; Diversity; Internet Of Things

Coverage

Dabbir R., Google LLC, San Jose, CA, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152605-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/NETCRYPT65877.2025.11102667

https://www.scopus.com/pages/publications/105015469770?origin=resultslist