International Journal of Contemporary Research in Multidisciplinary

Abstract:

The increasing volume and complexity of data in contemporary environments pose significant challenges to traditional data engineering methodologies. In response, this research explores the transformative potential of cognitive computing in data engineering applications. Cognitive computing, encompassing natural language processing, machine learning, and knowledge representation, offers a paradigm shift in how data is processed, analyzed, and utilized for decision-making. This paper investigates the fundamental principles of cognitive computing and its specific applications in data engineering workflows. The literature review examines the core components of cognitive computing and their relevance to data processing tasks. Key areas explored include the optimization of data cleaning, integration, and transformation processes, as well as the capability of cognitive computing to handle unstructured data formats effectively. Additionally, the paper delves into cognitive analytics and its role in advanced analytics, predictive modeling, and decision support systems. Real-world case studies across diverse industries illustrate the practical impact of cognitive computing on improving decision-making processes. Despite its promises, the integration of cognitive computing in data engineering presents challenges and ethical considerations. This research addresses concerns related to algorithmic bias, transparency, and privacy preservation, providing a comprehensive overview of the considerations necessary for responsible implementation. The methodology section outlines the research approach, including the selection of datasets, cognitive computing models, and performance evaluation metrics. Results and discussions encompass the assessment of cognitive computing models' performance through key metrics and comparative analyses against traditional data engineering methods. Real-world applications display the tangible impact of cognitive computing in healthcare, finance, manufacturing, and e-commerce. In conclusion, this research paper consolidates the findings, implications, and contributions of integrating cognitive computing into data engineering applications. It underscores the transformative potential of cognitive computing in addressing the challenges posed by big data and offers insights into the ethical considerations necessary for responsible deployment. The paper concludes by outlining future directions for advancements in the field, emphasizing the continuous evolution of cognitive computing in reshaping data engineering workflows.

Keywords:

Cognitive Computing, transformative, artificial intelligence, Convolutional Neural Networks

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Methods