会议信息

BDCloud 2026: International Conference on Big Data and Cloud Computing

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截稿日期:
2026-10-15 已截稿
通知日期:
2026-11-10
会议日期:
2026-12-27
会议地点:
Kuala Lumpur, Malaysia
届数:
浏览: 33714   关注: 10   参加: 2
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BDCloud
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会伴指数 (CP-I)

54.7 / 100
全站第 919 名 / 共 5,759 个会议 · 前 16%

数据挖掘与数据库 第 42 / 339 系统与体系结构 第 120 / 341

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
76
社区关注 (10%)
37
资料公开度 (15%)
55

用到的输入: 有据可查的届次：16 · 在会伴关注它的研究者：10 人 · 过去 24 个月打开过本页的研究者：5 人

公开资料里还缺: 历年录用率 (+4.5) · 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 45% —— 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 2026-10-25

征稿

BDCloud 2026 (International Conference on Big Data and Cloud Computing) is an academic conference held in Kuala Lumpur, Malaysia on 2026-12-27. The paper submission deadline is 2026-10-15. Acceptance notifications are sent on 2026-11-10.

Big data refers to datasets that exceed the processing capabilities of traditional software tools, characterized by diverse sources (Variety), large scale (Volume), and high-speed generation (Velocity). Ensuring its value (Value) and reliability (Veracity) is essential for effective decision-making. Meanwhile, cloud computing has emerged as a key platform for delivering IT resources and infrastructure as services, enabling cost-effective, on-demand access through pay-as-you-go models, reducing the need for significant capital investment. Topics of BDCloud 2026 include but are not limited to the following areas: Track 1: Cloud Computing Theory & Application – Fundamentals of cloud computing – Provisioning/pricing cloud models – Data storage in cloud computing – Resource scheduling in cloud computing – Fault tolerance and reliability in cloud computing – Access control to cloud computing – Monitoring and auditing in cloud Track 2: Cloud & Artificial Intelligence – AI-driven cloud services & operations – Use of cloud to improve AI deployment – Predictive analysis and federated learning – Automation for remediation & mitigation – Observability and transparency – Edge AI systems and applications – Cloud infrastructure for LLM Track 3: Data Privacy, Security, and Compliance – Encryption algorithms and secure communication – Homomorphic encryption – Data anonymization and de-identification – Access control and identity authentication – Defense mechanisms in cloud system – Security auditing and risk assessment – Privacy-preserving machine learning and AI models Track 4: Big Data Mining & Analytics – Big data visualization – Big data mining and analytics on cloud – Scalability of machine learning models – Distributed and federated datasets – Big data scheduling and optimization – Real-time data stream analysis – Big data applications
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