会议信息

EDBT 2027: International Conference on Extending Database Technology

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截稿日期:
2026-10-07 已截稿
通知日期:
2026-12-05
会议日期:
2027-04-06
会议地点:
Lille, France
届数:
30
CCF: B   ICORE: B   QUALIS: A2   浏览: 174373   关注: 116   参加: 19
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EDBT
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会伴指数 (CP-I)

73.5 / 100
全站第 287 名 / 共 5,764 个会议 · 前 5%

数据挖掘与数据库 第 15 / 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%)
92
社区关注 (10%)
75
资料公开度 (15%)
35

用到的输入: 收录等级：CCF B, ICORE B, QUALIS A2 · 有据可查的届次：30 · 在会伴关注它的研究者：116 人 · 过去 24 个月打开过本页的研究者：50 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
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 2026-11-02

征稿

EDBT 2027 (International Conference on Extending Database Technology) is a CCF B / ICORE B / QUALIS A2 conference held in Lille, France on 2027-04-06. The paper submission deadline is 2026-10-07. Acceptance notifications are sent on 2026-12-05.

The annual International Conference on Extending Database Technology (EDBT) is a leading international forum for researchers, developers, and users to present and discuss cutting-edge ideas, and to exchange techniques, tools, and experiences related to data management. The conference invites submissions of original research contributions related to all aspects of data management. A list of specific topics of interest is provided below and is by no means exhaustive:: Data Infrastructure & Systems Architecture Cloud data management High-availability systems Data management on modern hardware Distributed and parallel data management Storage, indexing, and physical database design Middleware and workflow management Sustainable and energy-efficient data management Data Processing & Query Management Query processing and optimization techniques Concurrency control, recovery, and transaction management Complex event processing and data streams Benchmarking and performance evaluation Data Models, Integration & Quality Data models and semantics Data integration, heterogeneous data management Schema mapping, matching, and integration Data quality, data profiling, data preparation, and data cleaning Data lakes and data discovery Metadata management and standards Query languages Provenance Uncertain, probabilistic, and approximate data Data management and AI Machine learning methods for data and database tuning Data-intensive and data-driven systems for ML/AI Tabular foundation models and LLMs Natural language processing for databases Multi-modal data Data management for ML/AI models Specialized Data Types & Domains Data lakes Graph data Spatial and temporal data Sensor, IoT, and mobile data Text data, extraction, and information retrieval Unstructured data and multimedia data Scientific and statistical data Analytics, Intelligence & Discovery Data mining and knowledge discovery Data warehousing, large-scale analytics, and ETL tools Process mining Knowledge graphs and knowledge management Semantic web and social networks Governance, Ethics & Human Interaction Privacy, security, and access control Responsible data management and ethics Data economy, data markets, data sovereignty Crowdsourced and collaborative data management Human-data interaction Data visualization, exploration, and user interfaces Usability, automaticity, interpretability, and explainability of data systems
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