A Comparative Analysis of Query Processing Techniques for Big Data (Published)
In the era of big data, efficient query processing is paramount for organizations seeking valuable insights from vast and diverse datasets. This paper presents a comprehensive comparative analysis of various query processing techniques tailored for big data environments. The study evaluates the performance, scalability, and adaptability of these techniques, shedding light on their strengths and weaknesses. Ultimately, this research serves as a valuable resource for data architects, engineers, and analysts faced with the task of selecting the most appropriate query processing technique for their big data projects. It provides insights into the trade-offs and considerations necessary to harness the full potential of big data analytics while optimizing resource utilization and query response times. The evaluation is conducted through a series of experiments on benchmark datasets and workloads that mimic real-world scenarios.
Keywords: Big Data
Innovations in Artificial Intelligence Research Fueled by Big Data Analytics (Published)
In recent years, the exponential growth of digital data has presented a remarkable opportunity and challenge for the field of artificial intelligence (AI). This paper provides a comprehensive examination of the profound influence of big data on AI advancements. With the advent of extensive data sources and improved data processing techniques, AI has made significant strides in various domains, including machine learning, natural language processing, computer vision, and more. This study reviews key developments in the intersection of big data and AI, offering insights into the ways in which big data has reshaped the landscape of AI research, applications, and industry. It assesses how AI-driven insights from massive datasets have not only improved decision-making processes but have also transformed entire industries by unlocking new opportunities and efficiencies. The synergy between artificial intelligence (AI) and big data analytics has ushered in a new era of innovation and transformative possibilities. This abstract presents a comprehensive overview of how big data analytics is driving breakthroughs in AI research, highlighting key advancements, applications, and implications.
Keywords: Artificial Intelligence, Big Data, deep learning, machine learning
Federated Query Processing in Big Data Integration Approaches and Prospects (Published)
The proliferation of big data and the diverse, distributed data sources that accompany it have given rise to the need for efficient and effective federated query processing in the context of big data integration. This paper explores the approaches and prospects of federated query processing, shedding light on the challenges, techniques, and future directions in this domain. Federated query processing is the art and science of seamlessly querying and retrieving data from heterogeneous, distributed data sources while maintaining performance, scalability, and data integrity. The challenges posed by the heterogeneity of data sources, varying data formats, and the distributed nature of big data have necessitated innovative approaches to enable effective query processing. This paper delves into various techniques and methodologies employed in federated query processing. It discusses data virtualization, query optimization, query rewrite, metadata management, and semantic integration as essential components of successful query federation. Additionally, it addresses the role of query federation middleware in orchestrating queries across distributed data sources. In conclusion, federated query processing plays a pivotal role in addressing the challenges of big data integration. The prospects of this approach are promising, enabling organizations to harness the full potential of their distributed data assets. As technology advances, federated query processing is poised to become an indispensable tool for organizations seeking to extract valuable insights from their growing repositories of big data.
Keywords: Big Data, Big Data Integration, Distributed Data Sources, Federated Query Processing
Demystifying Big Data: A Holistic Approach to Understanding, Processing, and Utilizing Data (Published)
The era of Big Data has ushered in a paradigm shift in the way information is generated, collected, and analyzed. This research paper, “Demystifying Big Data,” presents a comprehensive and holistic approach to unravel the complexities surrounding the vast expanse of data that characterizes our modern world. By examining the fundamental principles, challenges, and opportunities associated with Big Data, this paper aims to provide a roadmap for understanding, processing, and effectively utilizing the immense volumes of information available. The study begins by elucidating the key components of Big Data, including its volume, velocity, variety, veracity, and value. By delving into these dimensions, we offer readers a nuanced perspective on the multifaceted nature of Big Data, challenging prevalent misconceptions and fostering a deeper comprehension of its inherent characteristics. In doing so, we provide a solid foundation for professionals, researchers, and enthusiasts to navigate the intricate landscape of data analytics.
Keywords: Big Data, Data Insights, Data Streams, Digital Innovation