Call for Papers

The 38th International Conference on Scalable Scientific Data Management (SSDBM 2026)

The International Conference on Scalable Scientific Data Management (SSDBM 2026) brings together domain scientists, data management researchers, practitioners, and system developers to present and exchange the latest advances in scalable scientific data management. Building on the expanded vision introduced in 2025, the 38th edition continues to broaden its scope across all aspects of scalable and data-intensive scientific computing.

SSDBM has evolved from its origins as the International Conference on Scientific and Statistical Database Management into a premier venue for research at the intersection of data systems, scientific applications, and scalable computing, while retaining its well-recognized acronym.

SSDBM 2026 will serve as a forum for original research contributions, as well as practical system design, implementation, and evaluation of emerging techniques in scientific data management. The conference will maintain its single-track format to encourage active engagement and will feature invited talks, panel sessions, and demonstrations from both academia and industry.

SSDBM 2026 will be hosted by the San Diego Supercomputer Center (SDSC) at the University of California, San Diego in San Diego, California, from August 11 to August 13, 2026. Continuing the tradition of past SSDBM meetings, the conference provides a stimulating environment for fostering discussion, collaboration, and the exchange of ideas on all aspects of scientific and statistical data management, as well as high-performance data analysis tools and techniques for large-scale and distributed datasets.

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: up to 12 pages (including references and appendices)
• Short Papers: up to 6 pages (including references)

All submissions must follow the ACM format using the sigconf template: https://www.acm.org/publications/proceedings-template

For LaTeX users: \documentclass[sigconf,review]{acmart}

Submissions are single-blind reviewed; authors must include names and affiliations on the first page. Authors must comply with ACM policies on authorship and the use of generative AI tools and technologies.

Submission site: HotCRP (submission link will be available soon)

The program committee may recommend submissions as regular papers, short papers, or posters.

• Regular papers: full presentation at the conference
• Short papers: short presentation at the conference
• Posters: 1-page abstract and electronic poster presentation; published on the conference website (not in proceedings)

Regular and short papers will be included in the conference proceedings.

If a paper is accepted, at least one author must register for the technical program and present the paper in person. Each registration may cover only one accepted paper. Papers not presented at the conference will be removed from the proceedings.

Important Dates (AoE)

• Abstract Submission: April 24, 2026
• Paper Submission Deadline: May 1, 2026
• Notification: June 14, 2026