Conference Information

BIGML 2026: International conference on Big Data, Machine learning and Applications

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Submission Date:
2026-08-22
Notification Date:
2026-10-24
Conference Date:
2026-11-21
Location:
London, UK
Years:
Viewed: 20378   Tracked: 2   Attend: 0

Conference Partner Index (CP-I)

49.0 / 100
Ranked #1,767 of 5,680 conferences · Top 32%

#101 of 337 in Data Mining & Databases #178 of 739 in Artificial Intelligence & Machine Learning

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%)
56
Community attention (10%)
21
Public record completeness (15%)
55

Inputs used: Editions on record: 7 · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 3

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 2026-09-12

Call For Papers

BIGML 2026 (International conference on Big Data, Machine learning and Applications) is an academic conference held in London, UK on 2026-11-21. The paper submission deadline is 2026-08-22. Acceptance notifications are sent on 2026-10-24.

Scope & Topics 7th International conference on Big Data, Machine learning and Applications (BIGML 2025) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science and Information Technology. Topics of interest include, but are not limited to, the following: Big Data Big Data Techniques, models and algorithms Big Data Infrastructure and platform Big Data Search and Mining Big Data Security, Privacy and Trust Big Data Applications, Bioinformatics, Multimedia etc Big Data Tools and systems Big Data Mining Big Data Management Cloud and grid computing for Big Data Machine Learning and AI for Big Data Big Data Analytics and Social Media 5G and Networks for Big Data Machine Learning Machine Learning Applications Learning in knowledge-intensive systems Learning Methods and analysis Learning Problems Deep Learning
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