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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.02187 |
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| _version_ | 1866910725383913472 |
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| author | Hsieh, Weiche Bi, Ziqian Chen, Keyu Peng, Benji Zhang, Sen Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Liang, Chia Xin Ren, Jintao Niu, Qian Chen, Silin Yan, Lawrence K. Q. Xu, Han Tseng, Hong-Ming Song, Xinyuan Jing, Bowen Yang, Junjie Song, Junhao Liu, Junyu Liu, Ming |
| author_facet | Hsieh, Weiche Bi, Ziqian Chen, Keyu Peng, Benji Zhang, Sen Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Liang, Chia Xin Ren, Jintao Niu, Qian Chen, Silin Yan, Lawrence K. Q. Xu, Han Tseng, Hong-Ming Song, Xinyuan Jing, Bowen Yang, Junjie Song, Junhao Liu, Junyu Liu, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning, Machine Learning, Advancing Big Data Analytics and Management Hsieh, Weiche Bi, Ziqian Chen, Keyu Peng, Benji Zhang, Sen Xu, Jiawei Wang, Jinlang Yin, Caitlyn Heqi Zhang, Yichao Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Liang, Chia Xin Ren, Jintao Niu, Qian Chen, Silin Yan, Lawrence K. Q. Xu, Han Tseng, Hong-Ming Song, Xinyuan Jing, Bowen Yang, Junjie Song, Junhao Liu, Junyu Liu, Ming Machine Learning 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. |
| title | Deep Learning, Machine Learning, Advancing Big Data Analytics and Management |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.02187 |