Call for Papers

The International Conference on Scalable Scientific Data Management 2027 aims to bring together domain experts, data management researchers, practitioners, and developers to present and exchange the latest research findings on concepts, tools, and techniques for scalable scientific data management. The conference program typically features a single track to foster active discussion, and includes invited talks, panel sessions, and demonstrations of research prototypes and industrial systems.

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
• 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
• Innovations in data compression and encoding
• 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
• Data management for AI applications
• Machine learning and AI for scientific data management
• Data pipelines for deep learning and large-scale training workloads
• Visualization and interactive exploration of large datasets
• Security, privacy, and trust in scientific data systems

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
• Distributed systems and edge devices
• 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

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.

Submission site: TBA

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: TBA
• Submission deadline: TBA
• Notification: TBA