Related papers: Industrial DevOps
In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on…
Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to…
Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data…
Construction frequently appears at the bottom of productivity charts with decreasing indexes of productivity over the years. Lack of innovation and delayed adoption, informal processes or insufficient rigor and consistency in process…
This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering…
Cloud systems are the backbone of today's computing industry. Yet, these systems remain complicated to design, build, operate, and improve. All these tasks require significant manual effort by both developers and operators of these systems.…
As the Industry 4.0 takes shape, human operators experience an increased complexity of their daily tasks: they are required to be highly flexible and to demonstrate adaptive capabilities in a very dynamic working environment. It calls for…
Over the past thirty years, logistics has undergone tremendous change from a purely operational performance that led to sales or production and focused on securing supply and delivery lines to customers, with the transformation of…
DevSecOps, as the extension of DevOps with security training and tools, has become a popular way of developing modern software, especially in the Internet of Things arena, due to its focus on rapid development, with short release cycles,…
Using novel establishment-level observational data from Switzerland, we empirically examine whether the usage of key technologies of Industry 4.0 distinguishes across firms with different types of organizational culture. Based on the…
A single vendor cannot provide complete IIoT end-to-end solutions because cooperation is required from multiple parties. Interoperability is a key architectural quality. Composability of capabilities, information and configuration is the…
In this position paper, we explore the socio-technical interplay of Industry 4.0. Industry 4.0 is an industrial plan that aims at automating the production process by the adoption of advanced leading-edge technologies down the assembly…
The real-time data collection and automation capabilities offered by the Internet of Things (IoT) are revolutionizing and transforming Business Processes (BPs) into IoT-enhanced BPs, showing high potential for improving sustainability.…
The pivotal key to the success of manufacturing enterprises is a sustainable and innovative product design and development. In collaborative design, stakeholders are heterogeneously distributed chain-like. Due to the growing volume of data…
DevOps has become a dominant paradigm in modern software engineering, while low-code development platforms (LCDPs) are increasingly adopted to streamline software development. The integration of these approaches promises efficiency gains…
Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still…
With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the…
Widespread adoption of agile project management, independent delivery with microservices, and automated deployment with DevOps has tremendously speedup the systems development. The real game-changer is continuous integration (CI),…
For Industry 4.0 applications, communication protocols and data formats even for legacy devices are fundamental. In this paper, we focus on the Modbus/TCP protocol, which is, e.g., used in energy metering. Allowing Industry 4.0 applications…
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufacturing of highly customized products, the real-time control…