The workshop aims to explore the recent trends to design and implement, on the one side, systems for efficiently managing large amounts of data and extracting useful knowledge from this data, and, on the other side, intelligent systems by exploiting artificial intelligence techniques. The workshop expands the knowledge portfolio, in both computer infrastructures for intensive data management and methods for data analytics and artificial intelligence, allowing participants to interact with professionals from different backgrounds in different domains and contexts, where data processing is required. It focuses on the intersection of AI and data engineering in the context of smart computing, elaborating how AI techniques and technologies can be leveraged to enhance data engineering processes and enable the development of intelligent and adaptive systems for smart computing applications. The workshop focuses, also, on the integration of multiagent systems and data engineering to harness collaborative intelligence in data-rich environments. Participants will have the opportunity to delve into cutting-edge research, emerging trends, and practical applications in AI-based data engineering.
Participants will have the opportunity to share their research findings, present case studies, and engage in collaborative discussions to address challenges and explore future directions in AI-based data engineering for smart computing. The workshop would foster knowledge sharing, networking, and collaboration among researchers, industry practitioners, and stakeholders in this emerging field.
Topics of interest
The workshop covers a wide range of topics related to AI-based data engineering for smart computing, including, but not limited to:
Data cleansing, data augmentation, data integration and fusion and data imputation.
Data Engineering for Generative AI, Post-generative AI and Explainable AI.
Data Engineering for Real-time analytics and stream processing.
Data governance and ethics, Trustworthy AI
Data privacy preservation, data publishing, fairness, transparency, and bias mitigation.
Multiagent systems in data-intensive environments, data-driven agent behavior, learning agents, decision-making capabilities.
Collective intelligence in data engineering, collective knowledge, data engineering processes optimization, real-time coordination, and data sharing
Privacy and security in multiagent data systems, data privacy, security, trust in multiagent data engineering environments.