Conference Information

SSDBM 2027: International Conference on Scalable Scientific Data Management

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Submission Date:
2027-03-15 Due in 130 days
Notification Date:
2027-04-19
Conference Date:
2027-07-19
Location:
Singapore
Years:
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CCF: C   ICORE: B   QUALIS: A2   Viewed: 55945   Tracked: 60   Attend: 10
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SSDBM
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Conference Partner Index (CP-I)

76.2 / 100
Ranked #251 of 5,769 conferences · Top 5%

#14 of 339 in Data Mining & Databases

Academic recognition (35%)
92
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
99
Community attention (10%)
59
Public record completeness (15%)
55

Inputs used: Listed as CCF C, ICORE B, QUALIS A2 · Editions on record: 39 · Researchers following it here: 60 · Researchers who opened this page in the past 24 months: 12

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-05

Call For Papers

SSDBM 2027 (International Conference on Scalable Scientific Data Management) is a CCF C / ICORE B / QUALIS A2 conference held in Singapore on 2027-07-19. The paper submission deadline is 2027-03-15. Acceptance notifications are sent on 2027-04-19.

The International Conference on Scalable Scientific Data Management 2027 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 program typically features a single track to foster active discussion, and includes invited talks, panel sessions, and demonstrations of research prototypes and industrial systems. SSDBM 2027, the 39th edition of the conference, will be hosted by Nanyang Technological University (NTU), Singapore, from July 19 to July 21, 2027. 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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