Conference Information

ICDE 2027: International Conference on Data Engineering

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Submission Date:
2026-11-11 Due in 38 days
Notification Date:
2027-02-10
Conference Date:
2027-05-17
Location:
Copenhagen, Denmark
Years:
CCF: A   ICORE: A*   QUALIS: A1   Viewed: 347596   Tracked: 420   Attend: 57

Conference Partner Index (CP-I)

97.3 / 100
Ranked #3 of 5,693 conferences · Top 1%

#1 of 337 in Data Mining & Databases

Academic recognition (35%)
100
Submission selectivity (20%)
92
Editions held (20%)
100
Community attention (10%)
89
Public record completeness (15%)
100

Inputs used: Listed as CCF A, QUALIS A1 · Acceptance rate: 18.5% (mean of 1 editions on file) · Editions on record: 43 · Researchers following it here: 420 · Researchers who opened this page in the past 24 months: 90

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-03

Call For Papers

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.
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 18.5% over 1 years (2007–2007).

YearSubmittedAcceptedAccepted(%)
200765912218.5%

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