Related papers: Need for Design Patterns: Interoperability Issues …
With the growing scale and complexity of high-performance computing (HPC) systems, resilience solutions that ensure continuity of service despite frequent errors and component failures must be methodically designed to balance the…
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data,…
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural…
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies…
Because of the importance of object oriented methodologies, the research in developing new measure for object oriented system development is getting increased focus. The most of the metrics need to find the interactions between the objects…
Because of the importance of object oriented methodologies, the research in developing new measure for object oriented system development is getting increased focus. The most of the metrics need to find the interactions between the objects…
Data in the real world often has an evolving distribution. Thus, machine learning models trained on such data get outdated over time. This phenomenon is called model drift. Knowledge of this drift serves two purposes: (i) Retain an accurate…
The data science revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design…
Patterns describe proven solutions for recurring problems. Typically, patterns in a particular domain are interrelated and organized in pattern languages. As real-world problems often require patterns of multiple domains, different pattern…
Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their…
This paper explores the evolving landscape of data spaces, focusing on key concepts, practical applications, and emerging future directions. It begins by introducing the foundational principles that underpin data space architectures,…
Simulations offer opportunities in the examination of manufacturing processes. They represent various aspects of the production process and the associated production systems. However, often a single simulation does not suffice to provide a…
We propose a measurement framework for difficult-to-access contexts that uses indirect data traces, interpretable machine-learning models, and theory-guided triangulation to fill inaccessible measurement spaces. Many high-stakes systems of…
Testing and experimentation are crucial for promoting innovation and building systems that can evolve to meet high levels of service quality. IoT data that belong to users and from which their personal information can be inferred are…
Questions concerning the development of a logical model of innovation project data, as well as those concerning the design of information systems for decision-making support in the management of innovation projects, are discussed.
Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems…
The trajectory patterns of a moving object in a spatio-temporal domain offers varied information in terms of the management of the data generated from the movement. A trajectory data warehouse is a data repository for the data and…
Wearable devices are increasingly used as tools for biomedical research, as the continuous stream of behavioral and physiological data they collect can provide insights about our health in everyday contexts. Long-term tracking, defined in…
Observability is important to ensure the reliability of microservice applications. These applications are often prone to failures, since they have many independent services deployed on heterogeneous environments. When employed "correctly",…
Developing ontologies can be expensive, time-consuming, as well as difficult to develop and maintain. This is especially true for more expressive and/or larger ontologies. Some ontologies are, however, relatively repetitive, reusing design…