Data warehousing has undergone significant transformations since its inception, yet several critical areas remain unexplored. Traditional models, such as the Inmon and Kimball approaches, laid the foundation for structured data management. However, emerging...
Large amount of data available for the organization for which by which the same can influence its business decision. As large amount of data has been collected from distinguish resources and as the data has not been filtered which ultimately affect the...
Data governance has become a pivotal element in assessing the efficacy and enduring stability of IT systems in the contemporary data-centric landscape. It includes frameworks that assist organizations in overseeing data collection, storage, processing,...
Big data science has and is rapidly growing in the industries and is known to help in data analysis and forecasting. But the growth of data sharing in organizations creates many privacy problems especially in distributed environments. Discussing privacy...
The rapid growth of data across scientific, industrial, and social domains has created an increasing demand for professionals skilled in data mining and knowledge discovery. This proposal presents a structured and comprehensive data mining curriculum...
Impacted by globalisation, technological breakthroughs, and ever-changing customer expectations, the food business has experienced dramatic transformations over the past several decades. The use of big data and artificial intelligence (AI) has been more...
Cloud computing has moved from a peripheral IT decision to the default infrastructure assumption for financial institutions: 91% of financial-services firms globally have embarked on their cloud journey, up from just 37% in 2020, with North America leading...
Cloud computing has developed at a very fast pace and has transformed data engineering for organizations to handle big data. But limitations including reliability of data, the ability to expand the cloud-based systems, and security become major issues. This...
Due to the overwhelming number of solutions for Big Data processing during the last few years, distributed data engineering frameworks have become one of the most effective tools for real-time analyses and decision-making in numerous domains. However, these...
The recent increase in the use of big data and analytics in various global industries has created pressure on industries to develop reliable and flexible frameworks for data governance. Historically used approaches can be ineffective in addressing the...
Through the integration of analytical tools with operational and historical data, business intelligence systems provide crucial and competitive information to those responsible for corporate planning and decision-making. In order to help managers,...
Efficient anomaly detection in big data streams is critical for modern applications such as cybersecurity, Internet of Things (IoT), and financial fraud prevention. Traditional approaches struggle to handle the dynamic, high-dimensional, and interconnected...
The rapid growth of urbanization and globalization has amplified the complexity of modern transportation systems, necessitating innovative approaches to model and optimize these networks. This study explores the integration of Big Data and graph theory as a...
A lot of people across the world are talking about how AI and automation will affect the future of work, society, and the economy. We lay out the groundwork for artificial intelligence (AI) and big data (Big Data) and how they relate to public health in...
Multimorbidity, the coexistence of two or more chronic conditions in an individual, presents complex challenges for healthcare management and research. Traditional epidemiological and statistical methods often fail to capture the intricate interactions...
The future of smart cities promises unprecedented efficiency, connectivity, and sustainability through advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. However, as urban areas become...
A great deal of terminology pertaining to AI, ML, and deep learning is utilised extensively in today's corporate, healthcare, industrial, and military lexicons. No matter the size of the data, precise prediction and analysis are of the utmost importance in...
Data-driven research, together with its implications in machine learning and the broader domain of artificial intelligence, have the capacity to instigate significant transformations in medicine. Nevertheless, medicine is not a science akin to others. It is...
The explosive growth of social networks has led to the generation of vast, complex datasets, necessitating advanced methods for real-time analysis. Graph-based algorithms provide a natural framework for representing and analyzing social networks, with nodes...
The increasing social and economic influence of Big Data platforms has intensified the demand for decentralized alternatives. However, retrieving and querying information in decentralized environments requires fundamentally different techniques whose...
Multimorbidity denotes the simultaneous presence of two or more chronic illnesses inside an individual. Consequently, individuals with multimorbidity possess diverse and specific treatment requirements. In reality, fulfilling these objectives is challenging...
Artificial Intelligence (AI) has emerged as a transformative force reshaping governance frameworks across modern organizations. AI-Optimized Full-Stack Governance represents an advanced, integrated governance model that embeds AI-driven intelligence across...
Utilising AI, ML, and DL in Cutting-Edge Business Strategies: A Comprehensive Overview .This research delves deeply into the ways in which AI, ML, and DL influence the creation and refinement of company strategies. It delves into the ways AI impacts...
One of the most important models that have become significant for organizations with their IT service requirements is the hybrid cloud computing model. Hybrid models are defined as cloud computing combining smooth-running on-premises resources with public...
More effective and efficient solutions may be possible with the help of AI's new possibilities for in-situ and real-time learning and teaching. Therefore, AI may have an effect on the ways in which humans and machines work together to learn and teach, as...
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