Abstract
Abstract Data-Centric Engineering is an emerging branch of science that certainly will take on a leading role in data-driven research. We live in the Big Data era with huge amounts of available data and unseen computing power, and therefore a crafty combination of Statistics (or, in more modern terms, Data Science), Computer Science and Engineering is required to filter out the most important information, master the ever more difficult challenges of a changing world and open new paths. In this paper, we will highlight some of these aspects from a combined perspective of a statistician, an engineer and a software developer. In particular, we will focus on sound data handling and analysis, computational science in Structural Engineering, data care, security and monitoring, and conclude with an outlook on future developments.
Highlights
In 2012, IBM stated that 90% of the data available today have been generated over the past 2 years (Sagiroglu and Sinanc, 2013)
We live in the Big Data era with huge amounts of available data and unseen computing power, and a crafty combination of Statistics, Computer Science and Engineering is required to filter out the most important information, master the ever more difficult challenges of a changing world and open new paths
Technological advances combined with the creation of new powerful devices able to collect, store, and transfer huge amounts of data have re-shaped the landscape of our every
Summary
In 2012, IBM stated that 90% of the data available today have been generated over the past 2 years (Sagiroglu and Sinanc, 2013). The interest in data-driven research has been growing in the vast field of engineering (Chiaia et al, 2019; Madi and Radovanovi, 2012; Marrongelli et al, 2019), especially in combination with the advent of computational sciences This has led to the recently created new field of Data-Centric Engineering. One of the ultimate goals being the creation of digital twins, that is, a digital replica sharing the same physical properties as a real system, Data-Centric Engineering is, by nature, inter-disciplinary This poses a big challenge: one needs to combine knowledge from Engineering and Data Science in order to enter this domain, or alternatively seek for fruitful collaborations between researchers from each field, who by nature speak distinct scientific languages. We complete this paper in section “Conclusion” with a conclusion about the main chances and dangers of this new domain, and with our main message, namely that, in order for the field to stand a chance to flourish, a new type of targeted education is indispensable
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