Titlebook: New Frontiers in Quantitative Methods in Informatics; 7th Workshop, InfQ 2 Simonetta Balsamo,Andrea Marin,Enrico Vicario Conference proceed

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书目名称New Frontiers in Quantitative Methods in Informatics影响因子(影响力)




书目名称New Frontiers in Quantitative Methods in Informatics影响因子(影响力)学科排名




书目名称New Frontiers in Quantitative Methods in Informatics网络公开度




书目名称New Frontiers in Quantitative Methods in Informatics网络公开度学科排名




书目名称New Frontiers in Quantitative Methods in Informatics被引频次




书目名称New Frontiers in Quantitative Methods in Informatics被引频次学科排名




书目名称New Frontiers in Quantitative Methods in Informatics年度引用




书目名称New Frontiers in Quantitative Methods in Informatics年度引用学科排名




书目名称New Frontiers in Quantitative Methods in Informatics读者反馈




书目名称New Frontiers in Quantitative Methods in Informatics读者反馈学科排名

魔鬼在游行

Vs-Driven Big Data Process Developmenttime. To do so, the approach relies on annotating Big Data process workflows (and their individual elements) with relevant V-attribute values, which are then mapped into resource requirements and used in a performance model.

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Meditative

Conference proceedings 2018s. The papers are organized in topical sections on networking and mobile applications; applications of quantitative modeling; big data processing and IoT; theory, methods and tools for quantitative analysis.

现存

1865-0929 submissions. The papers are organized in topical sections on networking and mobile applications; applications of quantitative modeling; big data processing and IoT; theory, methods and tools for quantitative analysis.978-3-319-91631-6978-3-319-91632-3Series ISSN 1865-0929 Series E-ISSN 1865-0937

土产

Conference proceedings 2018 Italy, in December 2017..The 11 revised full papers and the one revised short paper presented were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on networking and mobile applications; applications of quantitative modeling; big data processing and

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Auto-Scaling in Data Stream Processing Applications: A Model-Based Reinforcement Learning Approachopose two model-based approaches and compare them to the baseline Q-learning algorithm. Our numerical investigations show that the proposed solutions provide better performance and faster convergence than the baseline.

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