Call for Short and Demo Papers

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

Short papers

Short papers are up to 6 pages (including references and appendices). Short papers should describe interesting, innovative ideas, which nevertheless require more work to mature, or are vision papers. Short papers will be given a short presentation slot in the conference and presented in the form of posters.

Demo papers

Demo papers are up to 6 pages (including references and appendices). Demo paper proposals should provide the motivation for the demonstrated concepts, the information about the technology and the system to be demonstrated (including a system description, functionality and figures when applicable), and should state the significance of the contribution. Selection criteria for the demonstration proposals evaluation include: the novelty, the technical advances and challenges, and the overall practical attractiveness of the demonstrated system. Demo papers will be given a short presentation slot in the conference and presented in the form of posters.

Submission Guidelines

Authors are invited to submit original, unpublished manuscripts.

• Short and Demo Papers are up to 6 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 \documentclass[sigconf,review]{acmart} configuration.
• SSDBM 2025 is single-blind reviewed; authors must include their names and affiliations on the first page. All authors should respect ACM Policy on Authorship and use of generative AI tools and technologies (details on the policy are available at ACM Policy on Authorship.

Submission site: EasyChair

Important Dates

• Submission deadline: March 11th, 2025 at 23:59 AoE (Anywhere on Earth)
• Notification: April 14th, 2025

Short & Demo papers will be given a presentation slot in the conference. Short & Demo papers (up to 6 pages including references) will be given an entry in the conference proceedings [Acceptance pending]. If your paper is selected, at least one author must register for the Technical Program in order to attend the conference and present the paper. Each author registration can be applied to only one accepted submission. For an accepted paper to be included in the proceedings, the author has to present the paper at the conference in person. Otherwise, the paper will be removed from the proceedings.