Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows a advantages and limitations.
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
Texas A&M University at Qatar（德克萨斯农工大学卡塔尔分校）；Virginia Tech（弗吉尼亚理工大学）；Universidad de Chile（智利大学）；Nanjing University of Science & Technology（南京理工大学）；InterDigital Communications, LLC.（InterDigital通信有限责任公司）
A Survey of Machine Learning for Big Code and Naturalness
Microsoft Research（微软研究院）；University College London（伦敦大学学院）；University of California, Davis（加州大学戴维斯分校）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
Zhejiang A&F University（浙江农林大学）；Lovely Professional University；Trinity College Dublin（三一学院）；Ho Chi Minh City University of Education and Technology（胡志明市教育与技术大学）；Northumbria University（诺森比亚大学）；University College Dublin（都柏林大学）；University of Oulu（奥卢大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Beijing Institute of Technology（北京理工大学）；Deakin University（迪肯大学）
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
Southwestern University of Finance and Economics（西南财经大学）；Chengdu University（成都大学）；Universität Hamburg（汉堡大学）；The University of Hong Kong（香港大学）；Hunan University of Technology and Business（湖南工业大学）；Xiangjiang Laboratory（湘江实验室）；Emory University（埃默里大学）
Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
Macau University of Science and Technology（澳门科技大学）；Hong Kong University of Science and Technology (HKUST)（香港科技大学）；Norwegian University of Science & Technology（挪威科技大学）；Sun Yat-sen University（中山大学）；Lulea University of Technology（吕勒奥理工大学）
NHANES-GCP: Leveraging the Google Cloud Platform and BigQuery ML for reproducible machine learning with data from the National Health and Nutrition Examination Survey
