Future industrial systems and applications are expected to be complex constellations of cyberphysical systems (CPSs) where intel l igent networked embedded devices play a pivotal role toward the realization of new sophisticated industrial scenarios. The prevalence of multifaceted devices enables new avenues for monitoring at large scale via Internet of Things (IoT) technologies, and, when coupled with the real-time analysis of massive amounts of data, it results in new insights that can enhance decision-making processes and provide a competitive business advantage. How to collect, process, analyze, and interpret big data is a challenge that affects all industries, and, if effectively addressed, it would offer numerous operational benefits. This article discusses some of the main architectural issues related to collecting and handling big data for analysis linked to IoT and cloud technologies in the industrial context. The aim is to provide a high-level introductory view of this topic, underpinned with examples from popular frameworks, and discuss open research questions and future directions.

中文翻译：

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工业信息物理系统：实现基于云的大数据基础设施

未来的工业系统和应用预计将成为网络物理系统 (CPS) 的复杂星座，其中智能联网嵌入式设备在实现新的复杂工业场景方面发挥着关键作用。多方面设备的普及为通过物联网 (IoT) 技术进行大规模监控提供了新途径，并且当与大量数据的实时分析相结合时，它会产生可以增强决策过程的新见解并提供有竞争力的业务优势。如何收集、处理、分析和解释大数据是一个影响所有行业的挑战，如果得到有效解决，它将带来许多运营收益。本文讨论了与收集和处理与工业环境中的物联网和云技术相关的分析大数据相关的一些主要架构问题。目的是提供该主题的高级介绍性观点，以流行框架的示例为基础，并讨论开放的研究问题和未来的方向。