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SSDBM 2027: International Conference on Scalable Scientific Data Management

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截稿日期:
2027-03-15 还有 130 天
通知日期:
2027-04-19
会议日期:
2027-07-19
会议地点:
Singapore
届数:
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CCF: C   ICORE: B   QUALIS: A2   浏览: 55949   关注: 60   参加: 10
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SSDBM
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会伴指数 (CP-I)

76.2 / 100
全站第 251 名 / 共 5,769 个会议 · 前 5%

数据挖掘与数据库 第 14 / 339

学术认可 (35%)
92
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
99
社区关注 (10%)
59
资料公开度 (15%)
55

用到的输入: 收录等级：CCF C, ICORE B, QUALIS A2 · 有据可查的届次：39 · 在会伴关注它的研究者：60 人 · 过去 24 个月打开过本页的研究者：12 人

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主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-11-05

征稿

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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