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Found 21 papers, 3 papers with code
Generating the Traces You Need: A Conditional Generative Model for Process Mining Data
In this work, we address this challenge by introducing a conditional model for process data generation based on a conditional variational autoencoder (CVAE).
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime.
Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles
Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions.
Recommending the optimal policy by learning to act from temporal data
Prescriptive Process Monitoring is a prominent problem in Process Mining, which consists in identifying a set of actions to be recommended with the goal of optimising a target measure of interest or Key Performance Indicator (KPI).
Nirdizati: an Advanced Predictive Process Monitoring Toolkit
Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs.
Leveraging pre-trained language models for conversational information seeking from text
Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on the construction of conversational information seeking (CIS) systems.
Learning to act: a Reinforcement Learning approach to recommend the best next activities
The rise of process data availability has recently led to the development of data-driven learning approaches.
PET: An Annotated Dataset for Process Extraction from Natural Language Text
Therefore, to bridge this gap, we present the PET dataset, a first corpus of business process descriptions annotated with activities, gateways, actors, and flow information.
Computing unsatisfiable cores for LTLf specifications
In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications.
Explainable Predictive Process Monitoring: A User Evaluation
The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.
Exploring Business Process Deviance with Sequential and Declarative Patterns
Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable outcomes.
Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges
The extraction of process models from text refers to the problem of turning the information contained in an unstructured textual process descriptions into a formal representation, i. e., a process model.
Process discovery on deviant traces and other stranger things
As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative.
A formalisation of BPMN in Description Logics
In this paper we present a textual description, in terms of Description Logics, of the BPMN Ontology, which provides a clear semantic formalisation of the structural components of the Business Process Modelling Notation (BPMN), based on the latest stable BPMN specifications from OMG [BPMN Version 1. 1 -- January 2008].
How do I update my model? On the resilience of Predictive Process Monitoring models to change
Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution.
Solving reachability problems on data-aware workflows
Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective.
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.
Predictive Process Monitoring Methods: Which One Suits Me Best?
Predictive process monitoring has recently gained traction in academia and is maturing also in companies.
Enhancing workflow-nets with data for trace completion
The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the scientific investigation towards techniques and tools which support more complex forms of process analysis.
Using Recurrent Neural Network for Learning Expressive Ontologies
Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation.
Abducing Compliance of Incomplete Event Logs
The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model.
