Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
Weiche Hsieh, Ziqian Bi, Keyu Chen, Benji Peng, Sen Zhang, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Yichao Zhang, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Chia Xin Liang, Jintao Ren, Qian Niu, Silin Chen, Lawrence K. Q. Yan, Han Xu, Hong-Ming Tseng, Xinyuan Song, Bowen Jing, Junjie Yang, Junhao Song, Junyu Liu, Ming Liu
TL;DR
The paper surveys how deep learning, machine learning, and big data analytics intersect to enable actionable insights from large, high-dimensional datasets. It synthesizes theoretical foundations, preprocessing pipelines, core analytics techniques, and scalable frameworks, with practical Python-based examples. Key contributions include a comprehensive treatment of data preprocessing (cleaning, integration, normalization, feature engineering), data warehousing concepts (ETL, schemas, cloud and big data integration), and a broad suite of data reduction and sampling methods (PCA, LDA, data cubes, various sampling strategies). The work emphasizes real-time analytics, governance, privacy (e.g., GDPR), and AI-enabled optimizations in data warehouses, highlighting practical architectures and future trends in cloud and edge-enabled ecosystems. Overall, it bridges theory and practice to equip researchers and practitioners with robust tools for modern data analytics across domains such as healthcare, finance, and policy-making.
Abstract
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies, emphasizing their role in uncovering actionable insights from massive, high-dimensional datasets. The study presents a systematic overview of data preprocessing techniques, including data cleaning, normalization, integration, and dimensionality reduction, to prepare raw data for analysis. Core analytics methodologies such as classification, clustering, regression, and anomaly detection are examined, with a focus on algorithmic innovation and scalability. Furthermore, the text delves into state-of-the-art frameworks for data mining and predictive modeling, highlighting the role of neural networks, support vector machines, and ensemble methods in tackling complex analytical challenges. Special emphasis is placed on the convergence of big data with distributed computing paradigms, including cloud and edge computing, to address challenges in storage, computation, and real-time analytics. The integration of ethical considerations, including data privacy and compliance with global standards, ensures a holistic perspective on data management. Practical applications across healthcare, finance, marketing, and policy-making illustrate the real-world impact of these technologies. Through comprehensive case studies and Python-based implementations, this work equips researchers, practitioners, and data enthusiasts with the tools to navigate the complexities of modern data analytics. It bridges the gap between theory and practice, fostering the development of innovative solutions for managing and leveraging data in the era of artificial intelligence.