Developing Conceptualizations of Operational Technology and Service Quality for Critical Infrastructures – »Devcon«

W. Schwinger, D. Graf, E. Kapsammer, B. Pröll, W. Retschitzegger, I. Khalil, A. Méndez-Porras, L. Cardinale-Villalobos, T. Juhana
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Abstract

Critical infrastructures (CIs) such as energy grids, communication networks or transportation systems are the essential backbone of any societies’ social and economic processes. Providing appropriate techniques to monitor the efficient and safe operation of their underlying Operational Technology (aka. OTM) is naturally of paramount importance. One crucial challenge is, however, that a CI’s OT is continuously evolving on a high pace, leading to massive heterogeneities, interdependencies and interoperability problems, thereby making OTM and integrative service quality hard to achieve. The research goal of our project »DevCon« is therefore to push forward appropriate techniques to facilitate OTM and integrative service quality even in face of the evolving nature inherent in this kind of systems. In particular, to overcome heterogeneities while explicating interdependencies and ensuring interoperability, conceptualizations of OT objects and service quality are put forward, providing the basis for a pivotal knowledge base. For this, an innovative approach is followed by synergistically combining inductive methods in terms of machine learning and deductive methods in terms of semantic technologies.
开发关键基础设施的运营技术和服务质量概念-“Devcon”
关键基础设施(CIs),如能源网、通信网络或运输系统,是任何社会的社会和经济进程的重要支柱。提供适当的技术来监控其底层操作技术(即。OTM)自然是最重要的。然而,一个关键的挑战是,CI的OT不断以高速度发展,导致大量的异构性、相互依赖性和互操作性问题,从而使OTM和集成服务质量难以实现。因此,我们的项目“DevCon”的研究目标是推动适当的技术,以促进OTM和综合服务质量,即使面对这种系统固有的不断发展的性质。特别是,为了克服异构性,同时解释相互依赖性和确保互操作性,提出了OT对象和服务质量的概念化,为关键知识库提供了基础。为此,一种创新的方法是协同结合机器学习方面的归纳方法和语义技术方面的演绎方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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