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Tuning Machine Learning to Address Process Mining Requirements
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4471-6750-1openalex-snapshot
Fundamentals of Predictive Text Mining
inferredcites →https://doi.org/10.1007/978-1-4757-2851-4_2openalex-snapshot
Independent Component Analysis
inferredcites →https://doi.org/10.1007/978-3-030-02671-4_6openalex-snapshot
Applying Sequence Mining for Outlier Detection in Process Mining
inferredcites →https://doi.org/10.1007/978-3-030-26619-6_16openalex-snapshot
Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications
inferredcites →https://doi.org/10.1007/978-3-030-33702-5_32openalex-snapshot
Simplification of Complex Process Models by Abstracting Infrequent Behaviour
inferredcites →https://doi.org/10.1007/978-3-030-35166-3_25openalex-snapshot
Activity Prediction of Business Process Instances with Inception CNN Models
inferredcites →https://doi.org/10.1007/978-3-030-40172-6_21openalex-snapshot
Academic View: Development of the Process Mining Discipline
inferredcites →https://doi.org/10.1007/978-3-030-55814-7_25openalex-snapshot
Analysis of Language Inspired Trace Representation for Anomaly Detection
inferredcites →https://doi.org/10.1007/978-3-030-58638-6_11openalex-snapshot
Predictive Process Mining Meets Computer Vision
inferredcites →https://doi.org/10.1007/978-3-030-58666-9_14openalex-snapshot
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction
inferredcites →https://doi.org/10.1007/978-3-030-58666-9_8openalex-snapshot
A Framework for Estimating Simplicity of Automatically Discovered Process Models Based on Structural and Behavioral Characteristics
inferredcites →https://doi.org/10.1007/978-3-030-66498-5_12openalex-snapshot
Root Cause Analysis in Process Mining Using Structural Equation Models
inferredcites →https://doi.org/10.1007/978-3-030-85082-1_15openalex-snapshot
Process Mining Encoding via Meta-learning for an Enhanced Anomaly Detection
inferredcites →https://doi.org/10.1007/978-3-030-85315-0_1openalex-snapshot
Concurrency and Objects Matter! Disentangling the Fabric of Real Operational Processes to Create Digital Twins
inferredcites →https://doi.org/10.1007/978-3-030-85469-0_25openalex-snapshot
A Framework for Explainable Concept Drift Detection in Process Mining
inferredcites →https://doi.org/10.1007/978-3-031-15743-1_12openalex-snapshot
Embedding Process Structure in Activities for Process Mapping and Comparison
inferredcites →https://doi.org/10.1007/978-3-031-17834-4_15openalex-snapshot
Validating Vector-Label Propagation for Graph Embedding
inferredcites →https://doi.org/10.1007/978-3-031-21686-2_11openalex-snapshot
Selecting Optimal Trace Clustering Pipelines with Meta-learning
inferredcites →https://doi.org/10.1007/978-3-031-34674-3_3openalex-snapshot
An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems
inferredcites →https://doi.org/10.1007/978-3-031-41623-1_7openalex-snapshot
Large Language Models for Business Process Management: Opportunities and Challenges
inferredcites →https://doi.org/10.1007/978-3-031-46846-9_1openalex-snapshot
Enhancing Fairness and Accuracy in Machine Learning Through Similarity Networks
inferredcites →https://doi.org/10.1007/978-3-031-50974-2_32openalex-snapshot
Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study
inferredcites →https://doi.org/10.1007/978-3-319-39696-5_23openalex-snapshot
Decision Mining Revisited - Discovering Overlapping Rules
inferredcites →https://doi.org/10.1007/978-3-319-56994-9_18openalex-snapshot
Event Abstraction for Process Mining Using Supervised Learning Techniques
inferredcites →https://doi.org/10.1007/978-3-319-59536-8_30openalex-snapshot
Predictive Business Process Monitoring with LSTM Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-65000-5_15openalex-snapshot
An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring
inferredcites →https://doi.org/10.1007/978-3-319-65000-5_18openalex-snapshot
Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions
inferredcites →https://doi.org/10.1007/978-3-319-98648-7_18openalex-snapshot
act2vec, trace2vec, log2vec, and model2vec: Representation Learning for Business Processes
inferredcites →https://doi.org/10.1007/978-3-642-00328-8_11openalex-snapshot
Trace Clustering in Process Mining
inferredcites →https://doi.org/10.1007/978-3-642-28108-2_19openalex-snapshot
Process Mining Manifesto
inferredcites →https://doi.org/10.1007/978-3-642-34156-4_10openalex-snapshot
Online Techniques for Dealing with Concept Drift in Process Mining
inferredcites →https://doi.org/10.1007/s10270-023-01132-2openalex-snapshot
From process mining to augmented process execution
inferredcites →https://doi.org/10.1007/s10462-021-09960-8openalex-snapshot
A systematic literature review on state-of-the-art deep learning methods for process prediction
inferredcites →https://doi.org/10.1007/s10586-018-2064-8openalex-snapshot
Analysis of alarms to prevent the organizations network in real-time using process mining approach
inferredcites →https://doi.org/10.1007/s12599-023-00795-xopenalex-snapshot
Welcome to the Era of ChatGPT et al.
inferredcites →https://doi.org/10.1007/s41060-022-00379-0openalex-snapshot
Process mining: software comparison, trends, and challenges
inferredcites →https://doi.org/10.1007/s41066-020-00226-2openalex-snapshot
Event abstraction in process mining: literature review and taxonomy
inferredcites →https://doi.org/10.1016/j.dss.2021.113669openalex-snapshot
Encoding resource experience for predictive process monitoring
inferredcites →https://doi.org/10.1016/j.dss.2022.113880openalex-snapshot
ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning
inferredcites →https://doi.org/10.1016/j.engappai.2023.106461openalex-snapshot
DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring
inferredcites →https://doi.org/10.1016/j.engappai.2023.107028openalex-snapshot
Trace encoding in process mining: A survey and benchmarking
inferredcites →https://doi.org/10.1016/j.eswa.2021.114934openalex-snapshot
A multi-perspective approach for the analysis of complex business processes behavior
inferredcites →https://doi.org/10.1016/j.inffus.2017.02.004openalex-snapshot
Ensemble learning for data stream analysis: A survey
inferredcites →https://doi.org/10.1016/j.infsof.2020.106472openalex-snapshot
Leveraging Small Sample Learning for Business Process Management
inferredcites →https://doi.org/10.1016/j.is.2018.04.003openalex-snapshot
To aggregate or to eliminate? Optimal model simplification for improved process performance prediction
inferredcites →https://doi.org/10.1016/j.procs.2019.12.173openalex-snapshot
Product Line Configuration Meets Process Mining
inferredcites →https://doi.org/10.1016/j.simpa.2023.100556openalex-snapshot
PM4Py: A process mining library for Python
inferredcites →https://doi.org/10.1016/j.softx.2023.101373openalex-snapshot
Cortado: A dedicated process mining tool for interactive process discovery
inferredcites →https://doi.org/10.1038/s42254-022-00441-7openalex-snapshot
Scientific machine learning benchmarks
inferredcites →https://doi.org/10.1109/3dv66043.2025.00119openalex-snapshot
3D-GPT: Procedural 3D Modeling with Large Language Models
inferredcites →https://doi.org/10.1109/access.2018.2865540openalex-snapshot
An Anti-Noise Process Mining Algorithm Based on Minimum Spanning Tree Clustering
inferredcites →https://doi.org/10.1109/access.2020.3025999openalex-snapshot
A Graph-Based Approach to Interpreting Recurrent Neural Networks in Process Mining
inferredcites →https://doi.org/10.1109/access.2020.3033450openalex-snapshot
Adversarial System Variant Approximation to Quantify Process Model Generalization
inferredcites →https://doi.org/10.1109/access.2023.3292117openalex-snapshot
Advancing Process Audits With Process Mining: A Systematic Review of Trends, Challenges, and Opportunities
inferredcites →https://doi.org/10.1109/cbi49978.2020.00016openalex-snapshot
Handling Concept Drift for Predictions in Business Process Mining
inferredcites →https://doi.org/10.1109/coase.2015.7294295openalex-snapshot
Discovery of patient pathways from a national hospital database using process mining and integer linear programming
inferredcites →https://doi.org/10.1109/ichi.2018.00009openalex-snapshot
A Data Quality Framework for Process Mining of Electronic Health Record Data
inferredcites →https://doi.org/10.1109/icpm49681.2020.00028openalex-snapshot
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
inferredcites →https://doi.org/10.1109/icpm49681.2020.00032openalex-snapshot
Anomaly Detection on Event Logs with a Scarcity of Labels
inferredcites →https://doi.org/10.1109/icpm57379.2022.9980737openalex-snapshot
Encoding High-Level Control-Flow Construct Information for Process Outcome Prediction
inferredcites →https://doi.org/10.1109/icws.2019.00069openalex-snapshot
Filtering Out Noise Logs for Process Modelling Based on Event Dependency
inferredcites →https://doi.org/10.1109/scc.2019.00037openalex-snapshot
Overlapping Analytic Stages in Online Process Mining
inferredcites →https://doi.org/10.1109/tsc.2015.2430327openalex-snapshot
A Co-Training Strategy for Multiple View Clustering in Process Mining
inferredcites →https://doi.org/10.1109/tsc.2015.2493732openalex-snapshot
Processes Meet Big Data: Connecting Data Science with Process Science
inferredcites →https://doi.org/10.1109/tsc.2017.2772256openalex-snapshot
Predictive Monitoring of Business Processes: A Survey
inferredcites →https://doi.org/10.1109/tsc.2020.3004532openalex-snapshot
Evaluation Goals for Online Process Mining: A Concept Drift Perspective
inferredcites →https://doi.org/10.1109/tsc.2021.3051771openalex-snapshot
A Multi-View Deep Learning Approach for Predictive Business Process Monitoring
inferredcites →https://doi.org/10.1109/tsc.2021.3139807openalex-snapshot
Deep Learning for Predictive Business Process Monitoring: Review and Benchmark
inferredcites →https://doi.org/10.1109/tsc.2024.3353817openalex-snapshot
Explainable and Effective Process Remaining Time Prediction Using Feature-Informed Cascade Prediction Model
inferredcites →https://doi.org/10.1137/1.9781611972795.35openalex-snapshot
Context Aware Trace Clustering: Towards Improving Process Mining Results
inferredcites →https://doi.org/10.1145/3301300openalex-snapshot
Outcome-Oriented Predictive Process Monitoring
inferredcites →https://doi.org/10.1145/3472752openalex-snapshot
A Survey on Concept Drift in Process Mining
inferredcites →https://doi.org/10.1186/s40537-021-00428-8openalex-snapshot
An alternative approach to dimension reduction for pareto distributed data: a case study
inferredcites →https://doi.org/10.18417/emisa.14.5openalex-snapshot
Repairing Outlier Behaviour in Event Logs using Contextual Behaviour
inferredcites →https://doi.org/10.2139/ssrn.3261050openalex-snapshot
Hedging the Drift: Learning to Optimize Under Non-Stationarity
inferredcites →https://doi.org/10.2307/2348250openalex-snapshot
The Box-Cox Transformation Technique: A Review
inferredcites →https://doi.org/10.32604/cmc.2021.015660openalex-snapshot
Adapted Long Short-Term Memory (LSTM) for Concurrent\\ Human Activity Recognition
inferredcites →https://doi.org/10.3390/bdcc5040061openalex-snapshot
Using Machine Learning in Business Process Re-Engineering
inferredcites →https://doi.org/10.3390/s20205770openalex-snapshot
A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition
inferredcites →https://doi.org/10.48550/arxiv.2011.13456openalex-snapshot
Score-Based Generative Modeling through Stochastic Differential Equations
inferredcites →https://doi.org/10.5220/0009979200050012openalex-snapshot
On the Pareto Principle in Process Mining, Task Mining, and Robotic Process Automation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Towards Enhanced Verification in Process Mining: A Ctmc-Based Probabilistic Model Checking Approach
inferred← citeshttps://doi.org/10.1007/978-3-032-13426-4_8openalex-snapshot
Leveraging Duration Pseudo-embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPM
inferred← citeshttps://doi.org/10.1007/978-3-032-23493-3_27openalex-snapshot
Predictive Business Process Monitoring with Transfer Learning
inferred← citeshttps://doi.org/10.1007/978-3-032-26996-6_14openalex-snapshot
Process Mining Powered by Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-032-37777-7_28openalex-snapshot
What Do Process Embeddings Learn? A Probing Investigation
inferred← citeshttps://doi.org/10.1007/978-3-658-48325-8_11openalex-snapshot
Enhancing IT Service Management Through Process Mining – A Digital Analytics Perspective on Documented Customer Interactions
inferred← citeshttps://doi.org/10.1007/s10844-024-00898-1openalex-snapshot
Conversing with business process-aware large language models: the BPLLM framework
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Context-Based Subvariant Discovery for Process Mining via Machine Learning
inferred← citeshttps://doi.org/10.1007/s41060-026-01142-5openalex-snapshot
Systematic review: a literature review of process mining in healthcare
inferred← citeshttps://doi.org/10.1007/s42979-026-04832-wopenalex-snapshot
A Deep Learning Framework for Predicting Business Process Violations
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128224openalex-snapshot
Leveraging a large language model (LLM) to predict hospital admissions of emergency department patients
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CICERONE: A natural language-based global approach for object-centric Predictive Process Monitoring
inferred← citeshttps://doi.org/10.1016/j.procs.2026.06.536openalex-snapshot
Learning from Change: Resilient Drift Detection and Adaptation in Process Mining
inferred← citeshttps://doi.org/10.1080/08874417.2024.2401049openalex-snapshot
Human Activity Recognition with Unsupervised Learning of Event Logs
inferred← citeshttps://doi.org/10.1109/access.2025.3538171openalex-snapshot
Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches
inferred← citeshttps://doi.org/10.1109/codit66093.2025.11321527openalex-snapshot
A Review of Process Mining and Machine Learning Integration for Corruption Detection in Business Processes
inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11391132openalex-snapshot
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes
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Predictive Information Management and Process Optimization Using Machine Learning Algorithms
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LUPIN: A LLM Approach for Activity Suffix Prediction in Business Process Event Logs
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Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis
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ProVEx: A Contrastive Explanation Framework for Process Variant Analysis
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A Design-Oriented Process Mining Framework for Railway Operations
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ANALYSIS OF MACHINE LEARNING APPLICATION IN THE MINING INDUSTRY
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Integration of Artificial Intelligence and Machine Learning for Enhancing Business in the Digital World
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