会议信息

ICBK 2018: IEEE International Conference on Big Knowledge

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ICBK
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截稿日期:
2018-06-20
通知日期:
2018-08-20
会议日期:
2018-11-17
会议地点:
Singapore
浏览: 21112   关注: 2   参加: 0

会伴指数 (CP-I)

43.1 / 100
全站第 3,659 名 / 共 5,655 个会议 · 前 65%
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%)
19
资料公开度 (15%)
25

用到的输入: 在会伴关注它的研究者：2 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 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 2026-09-09

征稿

ICBK 2018 (IEEE International Conference on Big Knowledge) is an academic conference held in Singapore on 2018-11-17. The paper submission deadline is 2018-06-20. Acceptance notifications are sent on 2018-08-20.

Aims and Scope Big Knowledge deals with fragmented knowledge from heterogeneous, autonomous information sources for complex and evolving relationships, in addition to domain expertise. The IEEE International Conference on Big Knowledge (ICBK) provides a premier international forum for presentation of original research results in Big Knowledge opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of Big Knowledge, including algorithms, software, systems, and applications. ICBK draws researchers and application developers from a wide range of Big Knowledge related areas such as knowledge engineering, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web. By promoting novel, high quality research findings, and innovative solutions to challenging Big Knowledge problems, the conference seeks to continuously advance the state-of-the-art in Big Knowledge. Accepted papers will be published in the conference proceedings by the IEEE Computer Society. Awards will be conferred at the conference on the authors of the best paper and the best student paper. A selected number of best papers will be invited for possible inclusion, in an expanded and revised form, in the Knowledge and Information Systems Journal. Topics of Interest Topics of interest include, but are not limited to: Foundations, algorithms, models, and theory of big knowledge processing. Knowledge engineering with big data. Machine learning, data mining, and statistical methods for big knowledge science and engineering. Acquisition, representation and evolution of fragmented knowledge. Fragmented knowledge modeling and online learning. Knowledge graphs and knowledge map. Topology and fusion on fragmented knowledge. Visualization, personalization, and recommendation of big knowledge navigation and interaction. Big knowledge systems and platforms, and their efficiency, scalability, and privacy. Applications and services of big knowledge in all domains including web, medicine, education, healthcare, and business. Big Health Care Decision Making Crowdsourcing Data-driven Granular Cognitive Computing Deep Learning Edge Computing in Medical Analyticsg Graph Mining Intelligent Computing for Big Knowledge Network and Knowledge Graph Representation Learning Rule and Relationship Discovery Safe Data Analytics Submission Guidelines Paper submissions should be no longer than 8 pages, in the IEEE 2-column format, including the bibliography and any possible appendices. Submissions longer than 8 pages will be rejected without review. All submissions will be reviewed by the Program Committee on the basis of technical quality, relevance to Big Knowledge, originality, significance, and clarity. All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is confidential. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks. Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.
由 Dou Sun 最后更新于

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