Conference Information

BDDM 2026: International Conference on Big Data and Data Mining

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Submission Date:
2026-09-17
Notification Date:
2026-10-27
Conference Date:
2026-11-27
Location:
Changsha, China
Years:
Viewed: 7243   Tracked: 1   Attend: 0

Conference Partner Index (CP-I)

46.7 / 100
Ranked #2,335 of 5,682 conferences · Top 42%

#134 of 337 in Data Mining & Databases

Academic recognition (35%) No data - scored at the neutral baseline of 50
Submission selectivity (20%) No data - scored at the neutral baseline of 50
Editions held (20%)
43
Community attention (10%)
23
Public record completeness (15%)
55

Inputs used: Editions on record: 4 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 8

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - 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 2026-09-19

Call For Papers

BDDM 2026 (International Conference on Big Data and Data Mining) is an academic conference held in Changsha, China on 2026-11-27. The paper submission deadline is 2026-09-17. Acceptance notifications are sent on 2026-10-27.

We invite submissions on a range of topics including, but not limited to, the following: Track 1: Big Data Big Data Analytics Artificial Intelligence Semantic Web Technologies for Big Data Big Data Search Algorithms and Systems Distributed and Peer-to-Peer Search Big Data-based Machine Learning Visual Analysis of Big Data Track 2: Data Mining Theory Data mining foundations Grand challenges of data mining Parallel and distributed data mining algorithms Mining on data streams Graph mining Spatial data mining Text, video, multimedia data mining Sequence processing and analysis Web mining High performance data mining algorithms Correlation mining Benchmarking and evaluations Interactive data mining Data-mining-ready structures and pre-processing Data mining visualization Information hiding in data mining Security and privacy issues Competitive analysis of mining algorithms Internet of things mining Personalization and recommendation systems Deep learning with small samples of Web data Knowledge graph
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