Scope 

The 17^th International Conference on Data, AI and Machine Learning Systems (DAIMLS 2026) invites high quality research contributions that push the boundaries of data systems, artificial intelligence, machine learning infrastructure and cloud native computing. As the world transitions into an era defined by large scale intelligence, powered by foundation models, retrieval augmented systems, autonomous agents and globally distributed cloud platforms, the need for integrated advances across data management, AI systems and ML engineering has never been more urgent.

DAIMLS 2026 serves as a premier global forum for researchers, practitioners and innovators to present ground-breaking ideas, share real world experiences and explore emerging trends across the full spectrum of intelligent data and AI systems. We welcome original, unpublished work spanning foundational theory, system design, performance engineering, applied AI and visionary perspectives.

The conference particularly encourages submissions that bridge traditionally separate communities’ databases, distributed systems, AI/ML systems, cloud computing, LLM infrastructure, vector search, data centric AI and autonomous agents, reflecting the interdisciplinary nature of modern intelligent computing.

Data Systems and Database Technologies
• Distributed, parallel and cloud native databases
• Serverless databases and elastic OLTP/OLAP
• Multi model databases (graph, document, time series, vector, key value)
• Query processing, optimization and self-driving databases
• In memory, NVM and CXL accelerated data systems
• Hardware accelerated data systems (GPU, FPGA, DPU, SmartNIC)
• AI native storage engines and computer architectures
Cloud Platforms and Large Scale Data Infrastructure
• Cloud native data platforms and cloud databases
• Multi cloud and cross cloud data federation
• Data lakes, lakehouses and unified analytics
• Scalable data processing engines (Spark, Flink, Ray, Dask)
• Disaggregated storage/compute architectures
• Geo distributed data and global inference systems
• Energy efficient and carbon aware data systems
• Cloud cost optimization and FinOps for AI/ML
AI Systems and Machine Learning Infrastructure
• Distributed ML training systems and large scale model training
• GPU/TPU/DPU accelerated ML infrastructure
• ML compilers, optimization and deep learning systems
• ML driven system optimization and learned components
• Feature stores, online ML and real time inference
• Memory efficient AI systems (quantization, pruning, distillation)
• Continual learning and lifelong model systems
• Mixture of Experts (MoE) training and serving systems
LLM, RAG and Intelligent Retrieval Systems
• Retrieval augmented generation (RAG) pipelines
• Vector databases and embedding management
• Hybrid retrieval (vector + graph + keyword)
• High throughput retrieval for LLM inference
• LLM augmented data systems (text to SQL, NL querying)
• RAG orchestrated workflows and pipelines
• Index selection and optimization for LLM/RAG workloads
Data Centric AI, Synthetic Data and ML Data Quality
• Data validation, debugging and quality for ML
• Weak supervision and programmatic labeling
• Data augmentation systems
• Synthetic data generation and evaluation
• Privacy preserving synthetic data pipelines
• Dataset versioning and curation for ML/LLMs
• Data contracts for AI/ML
Streaming, Real Time and Edge Intelligence
• Real time analytics and event processing
• IoT data management and edge cloud pipelines
• High velocity ingestion and low latency stream processing
• Real time ML inference and online feature computation
• Serverless AI systems and stateless ML pipelines
Security, Privacy and Responsible AI
• Differential privacy, MPC, FHE and secure enclaves
• Federated analytics and privacy preserving data lakes
• Secure data sharing, auditing and compliance
• Responsible AI, fairness and ethical data engineering
• Automated governance and compliance for AI pipelines
• AI safety, alignment and system level guardrails
• Red team simulation systems and adversarial testing
Knowledge Systems, Search and Intelligent Agents
• Knowledge graphs and semantic search
• Neural IR and multimodal retrieval
• Memory systems for autonomous agents
• Data systems for tool using AI agents
• Multi agent coordination and agentic workflows
• Long term knowledge storage and retrieval
Distributed, Federated and Decentralized Systems
• Federated databases and hybrid architectures
• Decentralized and blockchain based data systems
• Data marketplaces and secure data exchange
• Edge, fog and hybrid data management
Digital Twins, Simulation and Scientific Data
• Digital twin data management
• Simulation data pipelines
• Spatio temporal and high dimensional scientific data
• Data systems for robotics and autonomous vehicles
• Generative media data systems (video, audio, multimodal)
Workflow, Automation and Intelligent Orchestration
• Workflow management and process automation
• Event driven architectures and orchestration
• ML orchestrated data pipelines
• Intelligent data engineering automation
• Agent driven orchestration and decision systems
User Interaction, Visualization and Explainability
• Data visualization and exploratory analytics
• Explainable AI and explainable query processing
• Human in the loop data management
• Natural language interfaces for data and ML systems
• AI assisted data exploration and insight generation

Selected papers from DAIMLS 2026, after further revisions, will be published in the special issue of the following journals

• International Journal of Computer Science & Information Technology (IJDMS)
• International Journal of Data Mining & Knowledge Management Process (IJDKP)
• International Journal of Computer Science & Information Technology (IJCSIT)
• International Journal of Web & Semantic Technology (IJWesT)