In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible.
The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.
Table of Contents
- 1. Introduction
- 2. Recent Advances in Big Data Analytics
- 2.1 Scalable Data Processing Frameworks
- 2.2 Cloud-Based and Edge Computing Solutions
Objectives and Key Themes
This work aims to provide a comprehensive overview of recent advancements in Big Data analytics, Machine Learning (ML), and Deep Learning (DL). It explores the underlying methodologies, emerging trends, and transformative applications of these technologies, highlighting their impact across various sectors.
- Advancements in Big Data analytics and data processing frameworks.
- The role of Machine Learning and Deep Learning in automated decision-making and predictive modeling.
- Emerging trends in cloud and edge computing, and their impact on Big Data processing.
- Ethical considerations and challenges related to data privacy and computational scalability.
- Future directions in AI research and technological developments.
Chapter Summaries
1. Introduction: This introductory chapter sets the stage by discussing the rapid growth of digital data and the emergence of Big Data analytics, Machine Learning (ML), and Deep Learning (DL) as crucial technologies shaping modern industries and research. It highlights the confluence of Big Data and these learning paradigms, leading to groundbreaking applications across various sectors. The chapter also previews the exploration of advancements in scalable data processing, cloud and edge computing, AutoML, explainable AI, and cutting-edge DL models. Finally, it introduces the key challenges and ethical considerations associated with these technologies, outlining the chapter's scope and concluding by highlighting future directions in AI research and technological developments.
2. Recent Advances in Big Data Analytics: This chapter delves into the significant advancements in Big Data analytics and data science, focusing on dynamic trends shaping its future. It explores the shift towards real-time insights, the transformative impact of machine learning and artificial intelligence in automating decision-making across various sectors, and the challenges of managing increasingly diverse and voluminous data. The chapter further emphasizes the importance of data governance, the transformative potential of cloud technology, the rise of data marketplaces and the data mesh framework, and the revolutionary potential of Generative AI (GenAI) and retrieval-augmented generation (RAG). It concludes by highlighting the necessity for organizations to adapt and leverage big data analytics to fully realize its potential.
Keywords
Big Data, Machine Learning, Deep Learning, AutoML, Explainable AI, Transformer Models, Self-Supervised Learning, Generative Models, Cloud Computing, Edge Computing, Federated Learning, Data Privacy, Ethical AI, Scalable Data Processing, Artificial Intelligence Applications.
Frequently asked questions
What is the purpose of this document?
This document is a language preview for an academic work. It includes the title, table of contents, objectives and key themes, chapter summaries, and key words intended solely for academic use, specifically analyzing themes in a structured and professional manner.
What are the main topics covered in this work?
The work covers recent advancements in Big Data analytics, Machine Learning (ML), and Deep Learning (DL). It also explores methodologies, emerging trends, transformative applications, ethical considerations, data privacy, and computational scalability related to these technologies.
What are the objectives of this work?
The primary objective is to provide a comprehensive overview of recent advancements in Big Data analytics, Machine Learning (ML), and Deep Learning (DL). It aims to explore methodologies, emerging trends, and transformative applications, highlighting their impact across various sectors.
What are some of the key themes explored?
Key themes include advancements in Big Data analytics and data processing frameworks, the role of Machine Learning and Deep Learning in automated decision-making and predictive modeling, emerging trends in cloud and edge computing, ethical considerations related to data privacy and computational scalability, and future directions in AI research and technological developments.
What does Chapter 1 discuss?
Chapter 1 serves as an introduction, discussing the rapid growth of digital data and the emergence of Big Data analytics, Machine Learning (ML), and Deep Learning (DL). It highlights the confluence of Big Data and these learning paradigms, leading to groundbreaking applications across various sectors. It also previews the exploration of advancements in scalable data processing, cloud and edge computing, AutoML, explainable AI, and cutting-edge DL models and introduces key challenges and ethical considerations.
What does Chapter 2 cover?
Chapter 2 delves into the significant advancements in Big Data analytics and data science, focusing on dynamic trends shaping its future. It explores the shift towards real-time insights, the transformative impact of machine learning and artificial intelligence in automating decision-making across various sectors, and the challenges of managing increasingly diverse and voluminous data. The chapter further emphasizes the importance of data governance, the transformative potential of cloud technology, the rise of data marketplaces and the data mesh framework, and the revolutionary potential of Generative AI (GenAI) and retrieval-augmented generation (RAG).
What are some of the keywords associated with this work?
The keywords include Big Data, Machine Learning, Deep Learning, AutoML, Explainable AI, Transformer Models, Self-Supervised Learning, Generative Models, Cloud Computing, Edge Computing, Federated Learning, Data Privacy, Ethical AI, Scalable Data Processing, and Artificial Intelligence Applications.
What are some of the advancements in Big Data Analytics discussed?
Advancements discussed are real-time insights, machine learning and artificial intelligence impact, data governance, cloud technology impact, the rise of data marketplaces and the data mesh framework, and the revolutionary potential of Generative AI (GenAI) and retrieval-augmented generation (RAG).
- Citation du texte
- Rajesh Kumar Mishra (Auteur), Divyansh Mishra (Auteur), Rekha Agarwal (Auteur), 2025, Big Data, Machine, and Deep Learning, Munich, GRIN Verlag, https://www.grin.com/document/1572714
