{"title":"Data-driven Aspects of Engineering The Use of Operational Data in SoS Engineering: Chances and Challenges","authors":"M. Borth, E. V. Gerwen","doi":"10.1109/SYSOSE.2018.8428711","DOIUrl":null,"url":null,"abstract":"System of systems engineering moves towards the realization of smart systems. Information and its underlying data forms the backbone for such cyber-physical systems, especially within the application domains of the internet of things and of infrastructure systems of systems. Data therefore becomes a key component within system engineering. In this context, the rise of applied data science allows for novel uses of systems’ operational data for engineering purposes. We recognize several areas for which data-driven approaches yield promising results, but also identify challenges that render success difficult. These challenges, i.e., data sparsity, the skewness of statistical distributions w.r.t. relevant objects and the semantics of the context of systems, are in parts known to the data science community, but require an appropriate interpretation for engineering purposes. In our own work, we rely on existing domain knowledge to complement data driven aspects of system of systems engineering within a modelbased approach to address these and similar challenges.","PeriodicalId":314200,"journal":{"name":"2018 13th Annual Conference on System of Systems Engineering (SoSE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 13th Annual Conference on System of Systems Engineering (SoSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYSOSE.2018.8428711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
System of systems engineering moves towards the realization of smart systems. Information and its underlying data forms the backbone for such cyber-physical systems, especially within the application domains of the internet of things and of infrastructure systems of systems. Data therefore becomes a key component within system engineering. In this context, the rise of applied data science allows for novel uses of systems’ operational data for engineering purposes. We recognize several areas for which data-driven approaches yield promising results, but also identify challenges that render success difficult. These challenges, i.e., data sparsity, the skewness of statistical distributions w.r.t. relevant objects and the semantics of the context of systems, are in parts known to the data science community, but require an appropriate interpretation for engineering purposes. In our own work, we rely on existing domain knowledge to complement data driven aspects of system of systems engineering within a modelbased approach to address these and similar challenges.