A Comprehensive Survey on Advanced Data Science Platforms for Cyber-Physical Systems, Digital Twins, and Robotics

The integration of Cyber-Physical Systems (CPS), Digital Twins (DT), and robotics with Advanced Data Science Platforms (ADSP) is rapidly transforming industrial and research landscapes by enabling real-time data processing, intelligent decision-making, and enhanced automation. Over the past decades, with the growing demand for adaptable, expandable, and secure platforms, numerous groundbreaking studies have emerged in the field of ADSP for CPS, DT, and robotics. However, most existing research addresses isolated aspects of AI, CPS, DTs, or robotics, lacking a holistic view of how ADSP expands and optimizes these technologies. To address this gap, this survey provides a comprehensive and structured review of the existing methodologies, tools, and techniques at each step of the ADSP workflow, focusing on their applications in CPS, DT, and robotics. Firstly, this survey analyzes the data ingestion phase, focusing on raw data collection architecture, advanced data pre-processing techniques, and data lake integration, while identifying integration and scalability challenges. Secondly, the development methodologies and data analysis techniques within experimental workflows are explored by highlighting widely used tools and real-world case studies. Thirdly, a detailed overview of optimization techniques and deployment strategies is presented, including cloud, edge, and hybrid models, supported by practical deployment examples. Finally, the continuous learning mechanisms are investigated for adaptive system updates, challenges in real-time adaptation, and expanding model performance. Additionally, this survey focuses on evaluation metrics, benchmark studies, and performance comparisons to assess platform efficiency across various domains. This study also explores emerging challenges such as data quality & availability, platform expandability, model transparency, and security, offering insights into future research directions. To ensure the rigor and breadth, this survey is conducted on 316 high-quality studies, state-of-the-art methodologies, and platforms that are selected using the PRISMA methodology from over 600 reviewed publications. By presenting a structured overview of ADSPs, this work aims to serve as a valuable resource for researchers, engineers, and industry professionals aiming to utilize data science for innovation in CPS, DT, and robotics.