Related papers: Practical Process Capability Indices Workflows
Key performance indicators (KPIs), which can be extracted from the standardized interfaces of network equipment defined by current standards, constitute a primary data source that can be leveraged in the development of non-standardized new…
Estimating the execution time of software components is often mandatory when evaluating the non-functional properties of software-intensive systems. This particularly holds for real-time embedded systems, e.g., in the context of industrial…
This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. As the data are by nature curves, functional principal component analysis is a suitable candidate to…
Industrial components are of high importance because they control critical infrastructures that form the lifeline of modern societies. However, the rapid evolution of industrial components, together with the new paradigm of Industry 4.0,…
Ensuring consistent product quality in modern manufacturing is crucial, particularly in safety-critical applications. Conventional quality control approaches, reliant on manually defined thresholds and features, lack adaptability to the…
DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software…
Selecting optimal intervals of checkpointing an application is important for minimizing the run time of the application in the presence of system failures. Most of the existing efforts on checkpointing interval selection were developed for…
A dynamical system can be regarded as an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an…
Performativity of predictions refers to the phenomenon where prediction-informed decisions influence the very targets they aim to predict -- a dynamic commonly observed in policy-making, social sciences, and economics. In this paper, we…
Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, in an intuitive and…
While application profiling has been a mainstay in the HPC community for years, profiling of MPI and other communication middleware has not received the same degree of exploration. This paper adds to the discussion of MPI profiling,…
This paper introduces the concept of process variants in process-aware information systems (PAIS) during the design-time phase, where multiple variants of a single process must be specified. Today's organizations have to manage multiple…
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insight and hinder the integration of performance analytics into…
Provenance-based Intrusion Detection Systems (PIDSes) have been widely used to detect Advanced Persistent Threats (APTs). Although many studies achieve high performance in the evaluations of their original papers, their performance in…
We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of…
Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations…
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to a specific operating point,…
Many cluster similarity indices are used to evaluate clustering algorithms, and choosing the best one for a particular task remains an open problem. We demonstrate that this problem is crucial: there are many disagreements among the…
Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring…
Continuous Integration (CI) consists of an automated build process involving continuous compilation, testing, and packaging of the software system. While CI comes up with several advantages related to quality and time to delivery, CI also…