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12. April - 12. April 2026

Rang: B (CORE2023)Single-blind review; full papers up to 10 pages IEEE 2-column (references count); IEEE Computer Society proceedings manuscript format; electronic submission via WiLab Cyberchair

IEEE International Conference on Big Data

Aktualisiert: about 1 month ago
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Übersicht

IEEE BigData 2026 call for papers (https://bigdataieee.org/BigData2026/calls/papers/): solicits research on Big Data emphasizing the 5Vs, across foundations, infrastructure, management, mining, ecosystems, foundation models, applications, science use cases, benchmarks, and visionary work; separate Industrial & Government tracks with page limits as stated. Important dates: electronic full-paper submission Aug. 21, 2026; notification Oct. 24, 2026; camera-ready Nov. 14, 2026; conference Dec. 14–17, 2026. The page text incorrectly says the technical program will be at Sorrento, Italy; elsewhere the conference is Dec. 14–17, 2026 in Phoenix, AZ—treat Phoenix as authoritative for venue.

Call for Papers

IEEE BigData 2026 call for papers (https://bigdataieee.org/BigData2026/calls/papers/): solicits research on Big Data emphasizing the 5Vs, across foundations, infrastructure, management, mining, ecosystems, foundation models, applications, science use cases, benchmarks, and visionary work; separate Industrial & Government tracks with page limits as stated. Important dates: electronic full-paper submission Aug. 21, 2026; notification Oct. 24, 2026; camera-ready Nov. 14, 2026; conference Dec. 14–17, 2026. The page text incorrectly says the technical program will be at Sorrento, Italy; elsewhere the conference is Dec. 14–17, 2026 in Phoenix, AZ—treat Phoenix as authoritative for venue.

Wichtige Termine

Konferenzdaten

Conference Date

12. April 2026

Bisher:
  • 14. Dezember 2026
  • 8. Dezember 2025 - 11. Dezember 2025

Einreichung

Paper submission

NEU

8. Januar 2026

Benachrichtigung

Notification date

NEU

10. April 2026

Druckvorlage

Camera-ready

NEU

11. April 2026

Quellenrang

Quelle: CORE2023

Rang: B

Forschungsgebiet: Data management and data science, Machine learning, Artificial intelligence

Karte

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