
۰۲ تیر - ۰۴ تیر ۱۴۰۴
International Conference on Scientific and Statistical Data Base Management
هنوز دنبالکنندهای وجود ندارد.
نمای کلی
The 37th International Conference on Scalable Scientific Data Management (SSDBM 2025) will take place from June 23–25, 2025, in Columbus, Ohio, hosted by The Ohio State University. The conference serves as a forum for domain experts, data management researchers, practitioners, and developers to exchange the latest research on concepts, tools, and techniques for scalable and scientific data management, expanding its scope to cover all related fields.
SSDBM 2025: Call for Papers
The 37th International Conference on Scalable Scientific Data Management (SSDBM 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. The conference will be hosted by The Ohio State University in Columbus, Ohio, from June 23 to June 25, 2025.
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. 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.
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.
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
Submission Guidelines
Authors are invited to submit original, unpublished manuscripts.
- 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 employs a single-blind review process; 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 https://www.acm.org/publications/policies/new-acm-policy-on-authorship).
- Submission site: https://easychair.org/conferences/?conf=ssdbm2025.
Submission Types
- Regular Research Papers: Up to 12 pages (including references and appendices). These should be descriptions of complete technical work. Regular papers will be given a presentation slot in the conference and included in the conference proceedings.
- Short and Demo Papers: Up to 6 pages (including references). The program committee may decide to accept some papers from the main track as short papers. Short papers will be given a short presentation slot and included in the conference proceedings.
- Posters: Submissions should include a single-page abstract. Posters will have an electronic poster presentation and will be published on the conference website, without proceedings.
Note: 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.
Important Dates
All deadlines are Anywhere on Earth (AoE).
- Paper submission deadline (Main Track): March 2nd, 2025
- Paper submission deadline (Short and Demo papers): March 21st, 2025
- Poster submission deadline: April 21st, 2025 (extended)
- Notification for acceptance (Papers): April 4th, 2025
- Notification for acceptance (Short and Demo papers): April 14th, 2025
- Notification for acceptance (Posters): April 21st, 2025
- Camera-Ready Deadline: May 5th, 2025
- Author Registration Deadline: TBD, 2025
Registration
The registration site is currently open. Early bird registration ends on May 9th.
Organization Committee (Key Roles)
- General Chair: Suren Byna, The Ohio State University
- General Vice Chair: Anthony Kougkas, Illinois Institute of Technology
- Program Co-chairs:
- Venkat Vishwanath, Argonne National Laboratory
- Sarah Neuwirth, Johannes Gutenberg University Mainz
- Short and Demo Paper Co-chairs:
- Dong Dai, University of Delaware
- Jalil Boukhobza, ENSTA-Bretagne
- Alfredo Cuzzocrea, University of Calabria
- Student Posters Competition Co-chairs:
- Jay Lofstead, Sandia National Laboratory
- Karly Harrod, Oak Ridge National Laboratory
- Proceedings Chair: Jean Luca Bez, Lawrence Berkeley National Laboratory
- Web Chair: Houjun Tang, Lawrence Berkeley National Laboratory
- Publicity Chair: Chen Wang, Lawrence Livermore National Laboratory
تاریخهای کنفرانس
Conference Date
۲ تیر ۱۴۰۴ → ۴ تیر ۱۴۰۴
ارسال مقاله
Paper submission deadline (Main Track)
۱۲ اسفند ۱۴۰۳
Paper submission deadline (Short and Demo papers)
۱ فروردین ۱۴۰۴
Poster submission deadline
۱ اردیبهشت ۱۴۰۴
اعلان
Notification for acceptance (Papers)
۱۵ فروردین ۱۴۰۴
Notification for acceptance (Short and Demo papers)
۲۵ فروردین ۱۴۰۴
Notification for acceptance (Posters)
۱ اردیبهشت ۱۴۰۴
نسخه نهایی
Camera-Ready Deadline
۱۵ اردیبهشت ۱۴۰۴
رتبه منبع
منبع: CORE2023
رتبه: B
حوزه پژوهشی: Data management and data science