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Found 17 papers, 3 papers with code
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study
Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain.
Vector Quantile Regression on Manifolds
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
Geometric Epitope and Paratope Prediction
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules.
Performance-Preserving Event Log Sampling for Predictive Monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances.
Resolving Uncertain Case Identifiers in Interaction Logs: A User Study
Modern software systems are able to record vast amounts of user actions, stored for later analysis.
Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study
The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines.
Spectral Maps for Learning on Subgraphs
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
Process mining is a subfield of process science that analyzes event data collected in databases called event logs.
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data
Among the many sources of event data available today, a prominent one is user interaction data.
Process Mining on Uncertain Event Data
With the widespread adoption of process mining in organizations, the field of process science is seeing an increase in the demand for ad-hoc analysis techniques of non-standard event data.
Event Log Sampling for Predictive Monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances.
Probability Estimation of Uncertain Process Trace Realizations
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs.
Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
Text-Aware Predictive Monitoring of Business Processes
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems.
PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
The discipline of process mining aims to study processes in a data-driven manner by analyzing historical process executions, often employing Petri nets.
Conformance Checking over Uncertain Event Data
The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems.
Mining Uncertain Event Data in Process Mining
Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs.
