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Fair sharing of network resources among workflow ensembles
Computational science depends on complex, data intensive applications operating on datasets from a variety of scientific instruments. A major...
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Enhancing dynamic mode decomposition workflow with in situ visualization and data compression
Modern computational science and engineering applications are being improved by advances in scientific machine learning. Data-driven methods such as...
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Fast & Sound: Accelerating Synthesis-Rules-Based Process Discovery
Process discovery aims to construct process models describing the observed behaviors of information systems. It is an essential step in process...
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Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features
We propose the representation of data from finite element car crash simulations in a graph database to empower analysis approaches. The industrial...
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Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List Scheduling
This paper investigates the problem of dynamic workflow scheduling in cloud computing. In a real-time scenario, the only available information is...
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Stochastic Directly-Follows Process Discovery Using Grammatical Inference
Starting with a collection of traces generated by process executions, process discovery is the task of constructing a simple model that describes the...
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Plan, Generate and Match: Scientific Workflow Recommendation with Large Language Models
The recommendation of scientific workflows from public repositories that meet users’ natural language requirements is becoming increasingly essential...
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A discovery system for narrative query graphs: entity-interaction-aware document retrieval
Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an...
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Privacy-Preserving Truth Discovery with Task Hiding
Mobile crowdsensing has emerged as a popular platform for solving many challenging problems by utilizing users’ wisdom and resources. Due to the...
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Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application
AbstractMost process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result,...
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Defect graph neural networks for materials discovery in high-temperature clean-energy applications
We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the...
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ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph
The purpose of this work is to describe the orkg -Leaderboard software designed to extract leaderboards defined as task–dataset–metric tuples...
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MindSet: A Bias-Detection Interface Using a Visual Human-in-the-Loop Workflow
Handling data artifacts is a critical and unsolved challenge in deep learning. Disregarding such asymmetries may lead to biased and socially unfair...
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Monitoring a CI/CD Workflow Using Process Mining
Process mining (PM) is a unique approach to extract workflow models of actual real-world activities, namely those related to software development. To...
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Reproducibility Challenges of External Computational Experiments in Scientific Workflow Management Systems
Reproducibility is essential in scientific experiments to ensure that results can be consistently obtained using new data and methods across studies...
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Revolutionizing Drug Discovery: Unleashing AI’s Potential in Pharmaceutical Innovation
Artificial Intelligence (AI) technologies, including machine learning, and deep learning, have enabled the efficient analysis of vast datasets,...
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Unlocking the Potential of Generative Artificial Intelligence in Drug Discovery
Deep generative models have been widely employed across diverse fields, ranging from image and video analysis to natural language processing. In...
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Variable Discovery with Large Language Models for Metamorphic Testing of Scientific Software
When testing scientific software, it is often challenging or even impossible to craft a test oracle for checking whether the program under test...
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Incremental Discovery of Process Models Using Trace Fragments
Process discovery learns process models from event data and is a crucial discipline within process mining. Most existing approaches are fully...