SSDBM 2026 (International Conference on Scalable Scientific Data Management) is a CCF C / ICORE B / QUALIS A2 conference held in San Diego, California, USA on 2026-08-11. The paper submission deadline is 2026-04-24. Acceptance notifications are sent on 2026-06-14.
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
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