AI-Based Knowledge Discovery Frameworks for Complex Data Environments

Fredrik Heintz

AI-Based Knowledge Discovery Frameworks for Complex Data Environments

Author(s)	Fredrik Heintz
Country	Sweden
Abstract	The rapid expansion of digital technologies has resulted in the generation of increasingly large, heterogeneous, and complex datasets across business, healthcare, education, finance, manufacturing, scientific research, and public administration. Traditional analytical approaches often face limitations when attempting to identify meaningful patterns within high-dimensional, unstructured, distributed, and continuously changing data environments. Artificial intelligence provides advanced capabilities for discovering hidden relationships, extracting relevant knowledge, identifying anomalies, and generating predictive insights from complex datasets. This study examines AI-based knowledge discovery frameworks designed for complex data environments and proposes an integrated conceptual framework that combines data acquisition, preprocessing, representation, intelligent pattern discovery, knowledge extraction, validation, interpretation, and continuous learning. The research adopts a conceptual and descriptive methodology based on secondary scholarly literature concerning artificial intelligence, machine learning, deep learning, data mining, knowledge discovery, natural language processing, representation learning, and explainable AI. The analysis indicates that AI-driven knowledge discovery can improve the identification of hidden patterns, relationships, trends, anomalies, and predictive structures within complex datasets. However, data heterogeneity, noise, dimensionality, computational complexity, model interpretability, bias, privacy, and evolving data distributions create substantial challenges. The study concludes that effective AI-based knowledge discovery requires an integrated architecture capable of transforming heterogeneous data into validated, interpretable, and actionable knowledge. Continuous monitoring, explainability, robust data governance, and human validation are essential for developing reliable knowledge discovery systems.
Keywords	Artificial Intelligence, Knowledge Discovery, Complex Data, Machine Learning, Data Mining, Deep Learning, Pattern Recognition, Explainable AI, Intelligent Analytics
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-12

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American International Journal of Advanced Research and Development

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AI-Based Knowledge Discovery Frameworks for Complex Data Environments

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