会议信息

DSAA 2026: International Conference on Data Science and Advanced Analytics

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截稿日期:
2026-05-30 已截稿
通知日期:
2026-08-10
会议日期:
2026-10-06
会议地点:
New Delhi, India
届数:
13
CCF: C   ICORE: B   浏览: 55000   关注: 87   参加: 29
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会伴指数 (CP-I)

61.5 / 100
全站第 567 名 / 共 5,759 个会议 · 前 10%

数据挖掘与数据库 第 29 / 339

学术认可 (35%)
72
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
71
社区关注 (10%)
69
资料公开度 (15%)
35

用到的输入: 收录等级：CCF C, ICORE B · 有据可查的届次：13 · 在会伴关注它的研究者：87 人 · 过去 24 个月打开过本页的研究者：29 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 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 1.2 · 算于 2026-10-24

征稿

DSAA 2026 (International Conference on Data Science and Advanced Analytics) is a CCF C / ICORE B conference held in New Delhi, India on 2026-10-06. The paper submission deadline is 2026-05-30 (extended). Acceptance notifications are sent on 2026-08-10.

The DSAA'2026 Research Track solicits high-quality, original papers presenting novel issues of Data Science and Advanced Analytics across various disciplines and domains, including statistics, informatics, and computing, alongside shallow to deep representation, processing, analytics, learning, inference, optimization, visualization, and presentation. Topics of interests include but are not limited to: Data science foundations and theories Mathematics and statistics for data science and analytics Understanding data characteristics and complexities Machine/deep/statistical learning-based algorithms Advanced analytics and knowledge discovery methods Computer vision and pattern recognition Optimization theories and methods Large-scale databases, big-data processing, distributed processing, and analytics Model, analytics and learning actionability, reproducibility and provenance Theories and methods for evaluation, explanation, visualization, and presentation Ethical, trustworthy and responsible data analytics Submissions for the DSAA'2026 Research Track should very clearly specify the problem being solved, what methodologies were used to solve the problem, what data was used, how the results were evaluated, and how the solution is being used.
由 Dou Sun 最后更新于

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