The International Conference on Scalable Scientific Data Management 2025 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. While previous editions of SSDBM primarily focused on scientific statistical databases, the 37th edition in 2025 will broaden its scope to encompass all areas of scalable and scientific data management. Reflecting this expanded focus, the conference has been renamed from the International Conference on Scientific and Statistical Database Management to the International Conference on Scalable Scientific Data Management, while retaining the well-known acronym, SSDBM.

The new SSDBM conference will serve as a forum for original research contributions, as well as practical system design, implementation, and evaluation of the latest advances in scalable and scientific data management. The program typically features a single-track format to foster active discussion and includes invited talks, panel sessions, and demonstrations of research prototypes and industrial systems.

SSDBM 2025 will be hosted by The Ohio State University in Columbus, Ohio, from June 23 to June 25, 2025. Continuing the tradition of past SSDBM meetings, SSDBM 2025 will offer a stimulating environment designed to foster discussion, collaboration, and the exchange of ideas on all aspects of scientific and statistical data management research, as well as high-performance data analysis tools and techniques for distributed datasets.

The Proceedings of SSDBM 2025 will be published by Association of Computing Machinery (ACM) International Conference Proceeding Series (ICPS) and will appear in the ACM Digital Library and many indexing providers. [Acceptance pending]

Topics of Interest

Topics of particular interest include, but are not limited to, the following, as they relate to 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 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 in scientific data handling, and privacy in large-scale studies
Handling data errors, inconsistencies, and outliers in scientific datasets

Data Modeling, Management, and Integration

FAIR data principles (Findable, Accessible, Interoperable, Reusable)
Data lifecycle and retention management, provenance data management
Data integration
Data storage and management architectures (e.g., distributed file systems, data lakes, high-performance storage)
Protocols and frameworks for cross-domain data sharing and exchange
Modeling of scientific data
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 data
Scalable architectures and distributed systems for managing large-scale datasets
Optimization techniques for high-efficiency data storage and retrieval
Innovations in data compression and encoding for enhanced performance
Efficient computational techniques for statistical data analysis and modeling
Methods for ensuring data quality, integrity, and consistency in big data environments
Smart city applications and services leveraging high-performance data solutions

Machine Learning, Artificial Intelligence, and Visualization

Database support of machine learning and AI
Data management for AI applications
Machine learning and AI for scientific data management
Visualization and exploration of large datasets
Security and privacy in scientific data management
Data storage and compression techniques for machine learning

Streaming and Real-Time Data Processing

Stream data representation and management
Stream data analysis (e.g., summarization, statistical analysis, pattern matching, pattern discovery, learning, and prediction)
Dataflow and parallel processing for complex data workflows
Distributed systems and devices
Internet of Things (IoT) data analytics
Location-aware recommender systems