会议信息

BigData Congress 2019: International Congress on Big Data

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截稿日期:
2019-03-02 延期
通知日期:
2019-04-15
会议日期:
2019-07-08
会议地点:
Milan, Italy
届数:
8
浏览: 27310   关注: 14   参加: 3
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会伴指数 (CP-I)

46.5 / 100
全站第 2,463 名 / 共 5,697 个会议 · 前 44%

数据挖掘与数据库 第 141 / 336

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%)
59
社区关注 (10%)
34
资料公开度 (15%)
25

用到的输入: 有据可查的届次：8 · 在会伴关注它的研究者：14 人 · 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 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 1.1 · 算于 2026-10-12

征稿

BigData Congress 2019 (International Congress on Big Data) is an academic conference held in Milan, Italy on 2019-07-08. The paper submission deadline is 2019-03-02 (extended). Acceptance notifications are sent on 2019-04-15.

2019 IEEE International Congress on Big Data aims to provide an international forum for researchers and practitioners in academia and industry to present latest research findings and practice on major and emerging topics in Big Data. Big data is frequently described in terms of the four V's: volume, velocity, variety, and veracity. In this forum we encourage submissions that cover foundational advances in these four areas. We additionally seek papers that study the interplay of Big Data in the science and industry ecosystem, including infrastructure and frameworks, HPC, AI, convergence research, reproducibility, and data papers. Technical topics of interest include but are not limited to the following: Models and Algorithms Models and algorithms for big data analytics Models and algorithms for big data driven AI Data Systems Engineering From campus to cloud for big data analytics Cyberinfrastructure for big data AI and high performance computing for big data Big Data Management Data quality and integrity Provenance and workflows Security and Privacy System security and integrity Privacy-preserving architectures and analytics Usability / threat tradeoffs Service Performance Big data service performance evaluation Big data application benchmarks Big data service reliability and availability Real-time big data services Optimization and Visualization Optimization algorithms and systems with big data Visual analytics with big data Social media data mining Case Studies and Experiences Big data for enterprise, government, and society Big data for science and engineering research Architectures and policies for preservation of scientific data Case studies of value creation with open data Data papers for data sets
由 Dou Sun 最后更新于

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