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RDAAPS 2021: Reconciling Data Analytics, Automation, Privacy, and Security

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RDAAPS
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截稿日期:
2020-12-21
通知日期:
2021-02-22
会议日期:
2021-05-17
会议地点:
Hamilton, Ontario, Canada
浏览: 12132   关注: 0   参加: 0

会伴指数 (CP-I)

42.1 / 100
全站第 4,159 名 / 共 5,693 个会议 · 前 74%

数据挖掘与数据库 第 254 / 337 安全与隐私 第 260 / 362 机器人与控制 第 304 / 470

证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
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%) 无数据 —— 按中性基准 50 分计入 —
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 过去 24 个月打开过本页的研究者：2 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 25% —— 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 1.1 · 算于 2026-10-03

征稿

RDAAPS 2021 (Reconciling Data Analytics, Automation, Privacy, and Security) is an academic conference held in Hamilton, Ontario, Canada on 2021-05-17. The paper submission deadline is 2020-12-21. Acceptance notifications are sent on 2021-02-22.

The International Conference on RDAAPS is an annual forum on research in the broadly defined area of data analytics. RDAAPS brings together researchers from academia, industry, and public sector to present and discuss various aspects of data analytics, including privacy, security, and automation. This venue is meant to bring together stakeholders whose interests lie at the interface of these concerns, providing a platform for integrating the needs of industry with the state-of-the-art scientific advancements, and inspiring original research on solving enterprise data challenges. RDAAPS seeks papers presenting original research in the areas included, but are not limited to: Big Data Analytics for Decision Making New models and algorithms for data analytics Scalable data analytics Optimization methods in data analytics Theoretical analysis of data systems Analytical reasoning systems Decision making under uncertainty Learning systems for data analytics Large-scale text, speech, image, or graph processing systems Accountable Data Analytics Privacy-aware data analytics Fairness in data analytics Interpretable and transparent data analytics Incorporating legal and ethical factors into data analytics Strings in Data Analytics Patterns in Big Data Data compression Bioinformatics Algorithms and data structures for string processing Useful data structures for Big Data Data structures on secondary storage Security in Data Analysis Traceability of decision making Models for forecasting cyber-attacks and measuring impact Data usage in mounting security threats Data analytics for better situational awareness Domain knowledge modeling and generation Novel ontology representations Scalability of domain-based reasoning on big data Modeling and analyzing unstructured data sets Automation for data analytics, security, and privacy in manufacturing Application of data analysis in manufacturing Big data in Industry 4.0 Privacy and security in manufacturing Challenges of automation of data analytic processes Case studies of the automation of data analytics processes Architecture for data analytics and security Built-in privacy and security in data analytics automation
由 Dou Sun 最后更新于

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