Call for Papers

The International Conference on Scalable Scientific Data Management 2027 brings together domain experts, data management researchers, practitioners, and developers to present and exchange the latest research on concepts, tools, and techniques for managing, processing, and analyzing scientific data at scale. This includes data from simulation, observation, and experiment, as well as data produced and consumed by AI and agentic systems in science. The program includes research presentations, invited talks, panel sessions, and demonstrations of research prototypes and industrial systems, and is designed to encourage active discussion.

SSDBM 2027, the 39th edition of the conference, will be hosted by Nanyang Technological University (NTU), Singapore, from July 19 to July 21, 2027.

Topics of Interest

Topics of interest include, but are not limited to, the following areas in scientific data management:

Scientific Applications, Workflows, and Reproducibility

• Design, implementation, optimization, and reproducibility of scientific workflows
• AI-driven and agentic scientific workflows: planning, orchestration, and execution on HPC, cloud, and edge resources
• Provenance, auditability, and reproducibility of AI- and agent-generated results
• Platforms and tools for reproducible data science and scientific collaboration
• Application case studies (e.g., astrophysics, climate, energy, sustainability, biomedicine)
• Open data standards and cross-platform compatibility for scientific data
• Cloud and hybrid computing issues in large-scale data management
• System architectures for scientific data
• HPC applications and scalability challenges in data-intensive scientific fields
• Data ethics, bias, privacy, and responsible data use
• Handling data errors, inconsistencies, and uncertainty in scientific datasets

Data Modeling, Management, and Integration

• FAIR data principles (Findable, Accessible, Interoperable, Reusable)
• Data lifecycle and retention management, provenance tracking
• Data integration across heterogeneous sources
• Data storage and management architectures (distributed file systems, object stores, data lakes, high-performance storage)
• Protocols and frameworks for cross-domain data sharing and exchange
• Modeling of scientific data and schema evolution
• Information retrieval and text mining
• Indexing and querying scientific data, including spatial, temporal, and streaming data

Big Data Processing and Performance Aspects

• Big data processing frameworks for scientific workloads
• Scalable architectures and distributed systems for large-scale datasets
• Optimization techniques for efficient data storage and retrieval
• Data reduction and compression including error-bounded lossy compression
• Efficient computational techniques for statistical analysis and modeling
• Methods for ensuring data quality, integrity, and consistency at scale

Machine Learning, Artificial Intelligence, and Visualization

• Database and system support for machine learning and AI
• Machine learning and AI for scientific data management
• Data pipelines for deep learning and large-scale training workloads
• Data management for scientific foundation models: curation, deduplication, versioning, and lineage of training data
• LLM and agent interfaces to scientific data
• Storage and I/O for large-scale AI, including checkpointing, model and artifact management, and data loading at scale
• Reliability and trustworthiness of agents that act on scientific data: validation, guardrails, and human-in-the-loop control
• Visualization and interactive exploration of large datasets

Streaming and Real-Time Data Processing

• Stream data representation and management
• Stream data analysis (summarization, pattern discovery, prediction)
• Dataflow systems for complex and parallel workflows
• Internet of Things (IoT) data analytics
• Location-aware and real-time recommendation systems

Emerging Directions in Scientific Data Systems

• Cross-layer performance analysis and observability
• Data-centric system co-design across compute, memory, storage, and network
• Autonomous and self-optimizing data systems
• Multi-modal data management and analytics
• Digital twins and simulation-driven data pipelines
• Energy-efficient and sustainable infrastructure

Submission Guidelines

Authors are invited to submit original, unpublished manuscripts.

• Regular Research Papers are up to 12 pages (including references and appendices). Papers should be descriptions of complete technical work.
• All submissions should be formatted using the ACM format available at https://www.acm.org/publications/proceedings-template by selecting the generic “sigconf” sample. For submissions prepared in LaTeX, authors are recommended to use the \documentclass[sigconf,review]{acmart} configuration.
• SSDBM 2027 is single-blind reviewed; authors must include their names and affiliations on the first page.
• All authors should respect the ACM Policy on Authorship, including its policy on the use of generative AI tools and technologies. Please refer to the ACM Policy on use of AI.

Submission site: https://easychair.org/conferences/?conf=ssdbm2027

If your paper is selected, at least one author must register for the conference and present the paper in person. Otherwise, the paper will be removed from the proceedings.

Important Dates

• Abstract submission: March 1, 2027, 23:59 AoE
• Submission deadline: March 8, 2027, 23:59 AoE
• Notification: April 19, 2027
• Camera Ready: May 19, 2027