Laura Waltersdorfer

Laura Waltersdorfer
TU Wien | TU Wien

Dipl.-Ing.

About

19
Publications
635
Reads
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80
Citations
Introduction
Business Informatics / Information Systems / Systems Engineering

Publications

Publications (19)
Chapter
In agile Production Systems Engineering (PSE), multi-disciplinary teams work concurrently on various PSE artifacts in an iterative process that can be supported by common concept and Product-Process-Resource (PPR) modeling. However, keeping track of the interactions and effects of changes across engineering disciplines and their implications for ri...
Article
Full-text available
Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data c...
Chapter
Semantic Web Machine Learning Systems (SWeMLS) characterise applications, which combine symbolic and subsymbolic components in innovative ways. Such hybrid systems are expected to benefit from both domains and reach new performance levels for complex tasks. While existing taxonomies in this field focus on building blocks and patterns for describing...
Conference Paper
Full-text available
Research in neurosymbolic Artificial Intelligence (AI) approaches has surged recently: Symbolic and sub-symbolic methods are combined to solve complex tasks. Nevertheless the significance of this field, little systematised knowledge exists yet. To scope our research, we will focus on semantic web machine learning systems (SWeMLS). Furthermore , AI...
Conference Paper
Industry 4.0 envisions adaptive production systems,i.e.,Cyber-Physical Production Systems (CPPSs), to manufacture products from a product line. Product-Process-Resource modeling represents the essential aspects of a CPPS. However,due to discipline-specific models, e.g., mechanical, electrical, and automation models, it is often unclear how to integ...
Chapter
Both symbolic and subsymbolic AI research have seen a recent surge driven by innovative approaches, such as neural networks and knowledge graphs. Further opportunities lie in the combined use of these two paradigms in ways that benefit from their complementary strengths. Accordingly, there is much research at the confluence of these two research ar...
Conference Paper
Background. Systems modeling in Production Systems Engineering (PSE) is complex: Multiple views from different disciplines have to be integrated, while semantic differences stemming from various descriptions must be bridged. Aim. This paper proposes the Multi-view Modeling Framework (MvMF) approach and architecture of a model transformation pipelin...
Chapter
Background. In Production Systems Engineering (PSE) models, which describe plants, represent different views on several engineering disciplines (such as mechanical, electrical and software engineering) and may contain up to 10,000s of instance elements, such as concepts, attributes and relationships. Validating these models requires an integrated m...
Conference Paper
Technical Debt (TD) has proven to be a suitable communication concept for software-intensive contexts to raise awareness regarding longterm negative effects of deviations from standards and guidelines. TD has also been introduced to systems engineering domain, to communicate design shortcomings in long-running, software-assisted systems. We analyse...
Conference Paper
Background. In Production Systems Engineering (PSE), the planning of production systems involves domain experts from various domains, such as mechanical, electrical and software engineering collaborating and modeling their specific views on the system. These models, describing entire plants, can reach a large size (up to several GBs) with complex r...
Conference Paper
The correct representation of discipline-specific and cross-specific knowledge in manufacturing contexts is becoming more important due to inter-disciplinary dependencies and overall higher system complexity. How- ever, domain experts do seldom have sufficient technical and theoretical knowledge or adequate tool support required for productive and...
Chapter
In the parallel engineering of large and long-running automation systems, such as Production Systems Engineering (PSE) projects, engineering teams with different backgrounds work in a so-called Round-Trip Engineering (RTE) process to iteratively enrich and refine their engineering artifacts, and need to exchange data efficiently to prevent the dive...
Conference Paper
Multi-disciplinary data exchange still poses many challenges: Heterogeneous data sources, diverging data views, and lack of communication lead to defects, late resolving of errors, and mismatches over the project lifecycle. Semantic approaches such as ontologies are a viable solution to derive common concepts between disciplines to limit negative e...
Chapter
In the parallel engineering of industrial production systems, domain experts from several disciplines need to exchange data efficiently to prevent the divergence of local engineering models. However, the data synchronization is hard (a) as it may be unclear what data consumers need and (b) due to the heterogeneity of local engineering artifacts. In...

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