工业4.0中的数据管理：技术现状和面临的挑战
Data Management in Industry 4.0: State of the Art and Open Challenges
摘要：
信息和通信技术正在渗透到工业和制造系统的各个方面，加快了大量工业数据的生成。本文综述了数据管理应用于网络化工业环境的最新文献，并确定了未来几个开放的研究挑战。作为第一步，我们提取重要的数据属性（数量，种类，流量，关键性），并根据实际应用确定各种基本工业用例的相应数据支持技术。其次，我们提供了一个详细的概述，最近的工业建筑设计方面的数据管理哲学（数据存在，数据协调，数据计算）和他们的分布程度。然后，我们对最近的文献进行了全面的调查，从中我们得出了工业数据支持技术和以数据为中心的服务的最新进展的分类，从物理部署中的现场级别一直延伸到云和应用程序级别。最后，在这一批判性分析的丰富结论的激励下，我们确定了未来研究的有趣的开放性挑战。本文中介绍的概念主题涵盖了工业自动化金字塔层的大部分。我们的方法是多学科的，因为选定的出版物来自两个领域;通信、网络和计算领域以及工业、制造和自动化领域。本文可以帮助读者深入了解数据管理目前是如何在网络化工业环境中应用的，并选择有趣的开放式研究机会进行研究。
英文摘要：
Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
