Data-driven Design of Engineering Processes with COREPROModeler

Enterprises increasingly demand IT support for the coordination of their engineering processes, which often consist of hundreds up to thousands of sub-processes. From a technical viewpoint, these sub-processes have to be concurrently executed and synchronized considering numerous interdependencies. So far, this coordination has mainly been accomplished manually, which has resulted in errors and inconsistencies. In order to deal with this problem, we have to better understand the interdependencies between the subprocesses to be coordinated. In particular, we can benefit from the fact that sub-processes are often correlated to the assembly of a product (represented by a product data structure). This information can be utilized for the modeling and execution of so-called data-driven process structures. In this paper, we present the COREPRO demonstrator that supports the data-driven modeling of these process structures. The approach explicitly establishes a close linkage between product data structures and engineering processes

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Data-driven design of molecular nanomagnets

Three decades of research in molecular nanomagnets have raised their magnetic memories from liquid helium to liquid nitrogen temperature thanks to a wise choice of the magnetic ion and coordination environment. Still, serendipity and chemical intuition played a main role. In order to establish a powerful framework for statistically driven chemical design, here we collected chemical and physical data for lanthanide-based nanomagnets, catalogued over 1400 published experiments, developed an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied inferential statistical analysis. Our analysis shows that the Arrhenius energy barrier correlates unexpectedly well with the magnetic memory. Furthermore, as both Orbach and Raman processes can be affected by vibronic coupling, chemical design of the coordination scheme may be used to reduce the relaxation rates. Indeed, only bis-phthalocyaninato sandwiches and metallocenes, with rigid ligands, consistently present magnetic memory up to high temperature. Analysing magnetostructural correlations, we offer promising strategies for improvement, in particular for the preparation of pentagonal bipyramids, where even softer complexes are protected against molecular vibrations

Citizen data-driven design for pandemic monitoring

In a world concerned with the coronavirus pandemic, many governments do not know how to control the disease. Although there are several technologies that generate citizen data, transparency, and privacy are very important to ensure social engagement and more effectiveness in fighting the virus. This article analyzed some applications that contact tracing people or inform them about the disease. We selected the applications based on how they captured data, privacy issues, citizen participation, and the main challenges faced. Later, we created the app journey map to compare them and discovered the most used technology is Bluetooth, and the apps often have open source. However, these initiatives bring superficial insights and need to integrate with more complex data

Data-Driven Design to Production and Operation

Digital technology has introduced in the last decades data-driven representational and generative methodologies based on principles such as parametric definition and algorithmic processing. In this context, the 15th Footprint issue examines the development of data-driven techniques such as digital drawing, modelling, and simulation with respect to their relationship to design. The data propelling these techniques may consist of qualitative or quantitative values and relations that are algorithmically processed. However, the focus here is not on each technique and its respective representational and generative aspects, but on the interface between these techniques and design conceptualisation, materialization, and use

Direct data‐driven design of switching controllers

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