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Found 47 papers, 7 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.
ProMoAI: Process Modeling with Generative AI
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques.
Grouping Local Process Models
In recent years, process mining emerged as a proven technology to analyze and improve operational processes.
Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study
Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object.
Extracting Rules from Event Data for Study Planning
In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students.
TraVaG: Differentially Private Trace Variant Generation Using GANs
Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant.
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.
Comparing Ordering Strategies For Process Discovery Using Synthesis Rules
In this paper, we investigate the effect of different ordering strategies on the discovered models (w. r. t.
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.
Explainable Predictive Decision Mining for Operational Support
By predicting the decision, one can take proactive actions to improve the process.
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.
A Framework for Extracting and Encoding Features from Object-Centric Event Data
The flattening process is lossy, leading to inaccurate features extracted from flattened data.
Detecting Surprising Situations in Event Data
We define a process enhancement area as a set of situations (process instances or prefixes of process instances) where the process performance is surprising.
Clustering Object-Centric Event Logs
Therefore, several clustering techniques have been proposed on top of traditional event logs (i. e., event logs with a single case notion) to reduce the complexity of process models and discover homogeneous subsets of cases.
Predictive Object-Centric Process Monitoring
Objects in OCEL can have attributes that are useful in predicting the next event and timestamp, such as a priority class attribute for an object type package indicating slower or faster processing.
Detecting Context-Aware Deviations in Process Executions
We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods.
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.
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.
Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations
In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates.
Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities
The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks.
Precision and Fitness in Object-Centric Process Mining
Our precision and fitness notions are an appropriate way to generalize quality measures to the object-centric setting since we are able to consider multiple case notions, their dependencies and their interactions.
Probability Estimation of Uncertain Process Trace Realizations
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs.
Freezing Sub-Models During Incremental Process Discovery: Extended Version
Previously, an incremental discovery approach has been introduced where a model, considered to be under construction, gets incrementally extended by user-selected process behavior.
Free-Choice Nets With Home Clusters Are Lucent
The approach presented in this paper provides a novel perspective enabling new analysis techniques for free-choice nets that do not need to be well-formed.
A Framework for Explainable Concept Drift Detection in Process Mining
We propose a framework that adds an explainability level onto concept drift detection in process mining and provides insights into the cause-effect relationships behind significant changes.
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.
Process Comparison Using Object-Centric Process Cubes
Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes.
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.
OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs
We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
Discovering Object-Centric Petri Nets
Techniques to discover Petri nets from event data assume precisely one case identifier per event.
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.
Conformance Checking Approximation using Subset Selection and Edit Distance
This paper proposes new approximation techniques to compute approximated conformance checking values close to exact solution values in a faster time.
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.
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)
Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process.
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques.
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems.
Guided Interaction Exploration in Artifact-centric Process Models
For example, we are able to highlight strongly correlated behaviours in different artifacts.
Generating Time-Based Label Refinements to Discover More Precise Process Models
Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.
Mining Process Model Descriptions of Daily Life through Event Abstraction
However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts.
The Imprecisions of Precision Measures in Process Mining
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.
Event Stream-Based Process Discovery using Abstract Representations
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data.
Interest-Driven Discovery of Local Process Models
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes.
Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.
Event Abstraction for Process Mining using Supervised Learning Techniques
We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.
Log-based Evaluation of Label Splits for Process Models
We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.
Mining Local Process Models
The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.
