Search Results for author:
Found 7 papers, 6 papers with code
HOEG: A New Approach for Object-Centric Predictive Process Monitoring
To leverage this enriched data, we propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types.
Measuring the Stability of Process Outcome Predictions in Online Settings
This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring.
CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics
Current methods to generate counterfactual sequences either do not take the process behavior into account, leading to generating invalid or infeasible counterfactual process instances, or heavily rely on domain knowledge.
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection
We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches (such as Isolation forest and LOF) in the online setting.
All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees
Intuitively, the better the quality of the event data, the better the quality of the model that is discovered.
Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction
In this paper, we propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses.
Identifying Patient Groups based on Frequent Patterns of Patient Samples
In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert.
