文章基本信息

标题：Enabling Interactive Visualizations in Industrial Big Data *
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作者：Aguinaldo Bezerra ; Vitor Greati ; Vinícius Campos 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：11162-11167
DOI：10.1016/j.ifacol.2020.12.292
语种：English
出版社：Elsevier
摘要：AbstractIndustries are considered data rich but information poor environments. Mainly due to systems design restrictions, to the lack of adequate processing power and to a sector culture notably focused on collecting, selecting, storing and preserving historical series in on-demand access repositories, massive data generated in industrial operations is traditionally neglected (or simply took aside). This huge amount of unprocessed data resting in these repositories is a latent and rich source of information that could be used to improve industrial processes. This work then proposes an approach in which an elastic processing engine is designed to be plugged-in to currently installed industrial information infrastructure to provide it with the ability of performing visual analytics on massive industrial data. A case study where an interactive visualization application is made possible in real-world industrial data scenario of over 100 million records is presented to attest the effectiveness and potential of the proposed approach in enabling interactive visualizations to Industrial Big Data.
关键词：KeywordsKnowledge discover (data mining)intelligent decision support systems in manufacturingindustrial Big Datavisual analyticsdistributedparallel processing