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