会议信息

BDSEA 2016: IEEE/ACM International Conference on Big Data Science, Engineering, and Applications

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截稿日期:
2016-08-31 已截稿
通知日期:
2016-09-25
会议日期:
2016-12-06
会议地点:
Shanghai, China
届数:
3
浏览: 15806   关注: 7   参加: 2
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BDSEA
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会伴指数 (CP-I)

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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%)
28
资料公开度 (15%)
25

用到的输入: 有据可查的届次：3 · 在会伴关注它的研究者：7 人 · 过去 24 个月打开过本页的研究者：2 人

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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 2026-10-21

征稿

BDSEA 2016 (IEEE/ACM International Conference on Big Data Science, Engineering, and Applications) is an academic conference held in Shanghai, China on 2016-12-06. The paper submission deadline is 2016-08-31 (extended). Acceptance notifications are sent on 2016-09-25.

The IEEE/ACM International Conference on Big Data Science, Engineering, and Applications (BDSEA) is an annual international conference series. The first two events were held in London (BDC 2014) and Cyprus (BDC 2015) respectively. In 2016, the conference has been expanded to explicitly include application and renamed as BDSEA 2016. The conference series aims to provide a platform for researchers to present their new discoveries, developments, results, as well as the latest trends in big data computing and applications. BDSEA 2016 will be held in conjunction with the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016) at Tongji University, Shanghai, China. Authors are invited to submit original unpublished manuscripts on a broad range of topics related to big data science, computing paradigms, platforms and applications. Topics Topics of interest include, but are not limited to: I. Big Data Science Big Data Analytics Innovative Data Science Models and Approaches Data Science Practice and Experience Algorithms for Big Data Novel Big Data Search Techniques Innovative data and Knowledge Engineering approaches Data Mining and Knowledge Discovery Approaches for Big Data Big Data Acquisition, Integration, Cleaning, and Best Practices Experience reports in Solving Large Scale Data Science Problems II. Big Data Infrastructures and Platforms Scalable computing models, theories, and algorithms In-Memory Systems and platforms for Big Data Analytics Programming Systems for Big Data Cyber-Infrastructures for Big Data Performance evaluation reports for Big Data Systems Fault tolerance and reliability of Big Data Systems I/O and Data management Approaches for Big Data Energy-efficient Algorithms Storage Systems (including file systems, NoSQL, and RDBMS) Resource management Approaches for Big Data Systems Many-Task Computing Many-core computing and accelerators III. Big Data Security and Policy Big Data Archival and Preservation Big Data Management Policies Data Privacy Data Security Big Data Provenance Ethical and Anonymization Issues for Big Data Big Data Compliance and Governance Models IV. Big Data Applications Experience Papers with Big Data Application Deployments Big Data Applications for Internet of things Scientific application cases studies on Cloud infrastructure Big Data Applications at Scale Data streaming applications Mobile Applications of Big Data Big Data in Social Networks Healthcare Applications such as Genome processing and analytics Enterprise Applications V. Visualization of Big Data Visual Analytics Algorithms and Foundations Graph and Context Models for Visualization Analytical Reasoning and Sense-making on Big Data Visual Representation and Interaction Big Data Transformation, and Presentation
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