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ICDE 2027: International Conference on Data Engineering

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截稿日期:
2026-11-11 还有 31 天
通知日期:
2027-02-10
会议日期:
2027-05-17
会议地点:
Copenhagen, Denmark
届数:
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CCF: A   ICORE: A*   QUALIS: A1   浏览: 348369   关注: 422   参加: 58
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会伴指数 (CP-I)

97.4 / 100
全站第 3 名 / 共 5,695 个会议 · 前 1%

数据挖掘与数据库 第 1 / 336

学术认可 (35%)
100
投稿选择性 (20%)
92
会议传承 (20%)
100
社区关注 (10%)
90
资料公开度 (15%)
100

用到的输入: 收录等级：CCF A, QUALIS A1 · 录用率：18.5%（有记录的 1 届的均值） · 有据可查的届次：43 · 在会伴关注它的研究者：422 人 · 过去 24 个月打开过本页的研究者：99 人

置信度 100% —— 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 1.1 · 算于 2026-10-11

征稿

ICDE 2027 (International Conference on Data Engineering) is a CCF A / ICORE A* / QUALIS A1 conference held in Copenhagen, Denmark on 2027-05-17. The paper submission deadline is 2026-11-11. Acceptance notifications are sent on 2027-02-10.

Topics of Interest We invite the submission of original research contributions in the following areas of data engineering: Data models, semantics, and query languages Query processing and optimization Storage, indexing, and physical database design Transaction management, concurrency control, and recovery Benchmarking, performance modeling, tuning, and testing Distributed and parallel data management Cloud data management Energy-efficient data engineering Data systems on modern hardware Data streams, complex event processing, and edge computing Multidimensional data, data warehousing, OLAP, and analytics Time-series, temporal, spatial, and spatio-temporal data Uncertain, probabilistic, and approximate data Graph data Vector data Multi-modal and heterogeneous data AI and machine learning for data systems and vice versa Foundation models and LLMs for data engineering and vice versa Data mining with a data-centric focus Data visualization and interactive data exploration Knowledge graphs for data engineering Data preparation, cleaning, and integration Data quality, curation, provenance, and workflows Responsible and ethical data management Explainability, fairness, bias, trust, authentication, and regulatory compliance Data security and privacy Distributed ledgers and blockchains Data pricing and markets Domain-specific data engineering (scientific, medical, climate, urban, etc.) We expect that research contributions build on previous advances in the data engineering research community as published in ICDE or other core conferences such as VLDB and SIGMOD. We also welcome any original contributions that cross boundaries or point in other directions of interest to the data engineering research community.
由 Dou Sun 最后更新于

录用率

平均录用率: 18.5% 1 年间 (2007–2007).

年份提交数录用数录用率(%)
200765912218.5%

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