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BigSDM 2018: International Conference on Big Scientific Data Managemen

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截稿日期:
2018-09-30 Extended
通知日期:
2018-10-20
会议日期:
2018-11-30
会议地点:
Beijing, China
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征稿

BigSDM 2018 (International Conference on Big Scientific Data Managemen) is an academic conference held in Beijing, China on 2018-11-30. The paper submission deadline is 2018-09-30 (extended). Acceptance notifications are sent on 2018-10-20.

Topics The two-day conference provides an open forum to discuss the opportunities and challenges of a big scientific data ecosystem, and to explore best practices, standards, architectures and data infrastructures for fostering cross-disciplinary discovery and innovation. Topics of interest include, but are not limited to: • Paradigms for enhancing scientific discovery through big data • Framework for helping the science and AI communities to work together • Data cleansing and quality assurance of science data • Data sharing and preservation policy • Data management challenges posed by big scientific data • Machine learning methods to facilitate/accelerate scientific discovery • Streaming data, sensor networks and complex event processing • Scientific workflow management • Science platforms: (hardware/software) tools for data discovery and analysis from different angles • Storage system for large scale scientific applications • Cloud technology for big scientific applications • Scientific data visualisation Paper Submission The online submission system is: https://easychair.org/conferences/?conf=bigsdm2018. Authors may submit to either the full paper or short paper track. Full papers should be between 10 to 12 pages in length, and short papers between 6 to 8 pages. All submissions must be written in English and conform to the Springer LNCS proceedings format(http://www.springer.com/cn/computer-science/lncs/conference-proceedings-guidelines). Accepted papers will be published in LNCS proceeding.
由 Dou Sun 最后更新于

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Scientific Data6.9Springer2052-4463
Journal of Scientific Computing3.3Springer0885-7474
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CKnowledge-Based Systems7.2Elsevier0950-7051
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