会议信息

Big Data 2014: International Conference on Big Data

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截稿日期:
2014-04-30 已截稿
通知日期:
2014-06-05
会议日期:
2014-08-04
会议地点:
Beijing, China
届数:
3
浏览: 19061   关注: 0   参加: 0
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会伴指数 (CP-I)

39.8 / 100
全站第 5,208 名 / 共 5,759 个会议 · 前 91%

数据挖掘与数据库 第 310 / 339

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%)
37
社区关注 (10%)
10
资料公开度 (15%)
25

用到的输入: 有据可查的届次:3 · 过去 24 个月打开过本页的研究者:3 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 1.2 · 算于 2026-10-26

征稿

Big Data 2014 (International Conference on Big Data) is an academic conference held in Beijing, China on 2014-08-04. The paper submission deadline is 2014-04-30. Acceptance notifications are sent on 2014-06-05.

The 3rd ASE Conference series on Big Data Science and Computing(BigDataScience2014 Beijing) is the premier international forum for research on advancing the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets. The program contains two tracks for paper submission. SCOPE AND INTERESTS Topics of interest include, but are not limited to: Track 1 Big Data Science, Mining and Analytics -Big Data Science: Theories, models, algorithms, benchmarking, curation, and methods for understanding big data -Big Data Computing: Infrastructures, tools, programming, architectures, benchmarking, and testing of big data systems using Map/Reduce, Hadoop and others - Big Data Mining: Acquisition, representation, indexing, storage, management, processing, pre-processing and post-processing of big data - Big Data Analytics: Metrics, frameworks, evaluation, tools, analysis, visualization of big data - Big Data Understanding: learning, knowledge discovery, business and consumer intelligence, user behavior, community discovery Track 2 Big Data Applications - Big Data Applications: Industrial and scientific applications of big data such as search, recommendations, business intelligence, marketing, social media, healthcare, good practices and reproducibility. - Big Data Privacy and Security: Data privacy enhancing technologies, privacy-preserving computing, risk analysis, modeling, and manage-ment, trustworthy computing, access control
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