Process Mining Workshops : : ICPM 2021 International Workshops, Eindhoven, the Netherlands, October 31 - November 4, 2021, Revised Selected Papers.

• Superior document: Lecture Notes in Business Information Processing Series ; v.433
• Author: Munoz-Gama, Jorge.
• TeilnehmendeR: Lu, Xixi.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2022.; Ã2022.
• Year of Publication: 2022
• Edition: 1st ed.
• Language: English
• Series: Lecture Notes in Business Information Processing Series
• Physical Description: 1 online resource (419 pages)

Table of Contents

• Intro
• Preface
• Organization
• Contents
• XES 2.0 Workshop and Survey
• Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop
• 1 Introduction
• 2 XES Standard: A Brief Overview
• 3 Survey Design and Insights
• 4 Adding Context: Reflections from the XES 2.0 Workshop
• 5 Conclusion
• References
• EdbA 2021: 2nd International Workshop on Event Data and Behavioral Analytics
• Second International Workshop on Event Data and Behavioral Analytics (EdbA'21)
• Organization
• Workshop Chairs
• Program Committee
• Probability Estimation of Uncertain Process Trace Realizations
• 1 Introduction
• 2 Related Work
• 3 Running Example
• 4 Preliminaries
• 5 Method
• 6 Validation of Probability Estimates
• 7 Conclusion
• References
• Visualizing Trace Variants from Partially Ordered Event Data
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 Visualizing Trace Variants
• 4.1 Approach
• 4.2 Formal Guarantees
• 4.3 Limitations
• 4.4 Implementation
• 5 Evaluation
• 6 Conclusion
• References
• Analyzing Multi-level BOM-Structured Event Data
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 Methods
• 4.1 Analysis Methodology
• 4.2 M2BOM-Structured Assembly Processes
• 5 Case Study
• 6 Conclusion
• References
• Linac: A Smart Environment Simulator of Human Activities
• 1 Introduction
• 2 Existing Solutions
• 3 Proposed Simulation Solution
• 3.1 Configuration of the Smart Environment
• 3.2 Configuration of the Agents' Behavior - AIL Language
• 3.3 Simulation Execution
• 3.4 Clock Simulation
• 3.5 MQTT Output
• 4 Implementation
• 5 Evaluation
• 5.1 Configuration
• 5.2 Results
• 6 Conclusions and Future Works
• References
• Root Cause Analysis in Process Mining with Probabilistic Temporal Logic
• 1 Introduction
• 2 Related Work
• 3 The AITIA-PM Algorithm.
• 3.1 Background
• 3.2 Algorithmic Procedure
• 4 Demonstration
• 5 Conclusion
• References
• xPM: A Framework for Process Mining with Exogenous Data
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 A Framework for Process Mining with Exogenous Data
• 4.1 Linking
• 4.2 Slicing
• 4.3 Transformation
• 4.4 Discovery
• 4.5 Enhancing
• 5 Evaluation
• 5.1 Procedure
• 5.2 Quality Measures
• 5.3 Event Logs and Exogenous Data
• 5.4 Results and Discussion
• 6 Conclusion
• References
• A Bridging Model for Process Mining and IoT
• 1 Introduction
• 2 Background
• 2.1 IoT Ontologies
• 2.2 Business Process Context Modelling
• 3 Conceptual Ambiguity in IoT and PM
• 3.1 IoT Data
• 3.2 Context in PM vs Context in IoT
• 3.3 Process Event vs IoT Event
• 4 Connecting IoT and Process Mining: A Conceptual Model
• 5 Use Case Validation
• 6 Related Work
• 7 Conclusion
• References
• ML4PM 2021: 2nd International Workshop in Leveraging Machine Learning for Process Mining
• 2nd International Workshop in Leveraging Machine Learning for Process Mining (ML4PM 2021)
• Organization
• Workshop Chairs
• Program Committee
• Additional Reviewers
• Exploiting Instance Graphs and Graph Neural Networks for Next Activity Prediction
• 1 Introduction
• 2 Related Work
• 3 Methodology
• 3.1 Building Instance Graphs
• 3.2 Data Preprocessing
• 3.3 Deep Graph Convolutional Neural Network
• 4 Experiments
• 4.1 Experimental Setup
• 4.2 Results
• 5 Conclusions and Future Works
• References
• Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis
• 1 Introduction
• 2 Related Work
• 3 A Framework for Assessing the Generalisation Capacity of RNNs
• 3.1 The Resampling Procedure
• 3.2 Metrics
• 4 Experimental Evaluation
• 4.1 Process Models
• 4.2 Hyperparameter Search
• 4.3 Results
• 5 Discussion.
• 6 Conclusion and Future Work
• References
• Remaining Time Prediction for Processes with Inter-case Dynamics
• 1 Introduction
• 2 Preliminaries and Related Work
• 2.1 Related Work
• 2.2 RTM Background
• 2.3 Performance Spectrum with Error Progression
• 3 Approach
• 3.1 Detecting Uncertain Segments
• 3.2 Identifying Inter-case Dynamics in Uncertain Segments
• 3.3 Inter-case Feature Creation
• 3.4 Predicting the Next Segment
• 3.5 Predicting Waiting Time
• 4 Evaluation
• 4.1 Experimental Setup
• 4.2 Results
• 5 Conclusion
• References
• Event Log Sampling for Predictive Monitoring
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 Proposed Sampling Methods
• 5 Evaluation
• 5.1 Event Logs
• 5.2 Implementation
• 5.3 Evaluation Setting
• 5.4 Experimental Results
• 6 Discussion
• 7 Conclusion
• References
• Active Anomaly Detection for Key Item Selection in Process Auditing
• 1 Introduction
• 2 Related Work
• 2.1 Anomaly Detection
• 2.2 Active Anomaly Detection
• 2.3 Trace Visualisation
• 3 Active Selection Approach
• 3.1 Step One: Encode Process Data
• 3.2 Step Two: Assign Anomaly Score
• 3.3 Step Three: Actively Label Exceptions
• 4 Evaluation
• 4.1 Step One: Encode Process Data
• 4.2 Step Two: Assign Anomaly Score
• 4.3 Step Three: Actively Label Exceptions
• 4.4 Performance Results
• 5 Discussion
• 5.1 Cycle One
• 5.2 Cycle Two
• 5.3 Cycle Three
• 6 Limitations
• 7 Conclusion and Future Work
• References
• Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
• 1 Introduction
• 2 Background and Related Work
• 2.1 Predictive Process Monitoring
• 2.2 Prescriptive Process Monitoring
• 2.3 Causal Inference
• 3 Approach
• 3.1 Log Preprocessing
• 3.2 Predictive Model
• 3.3 Causal Model
• 3.4 Resource Allocator
• 4 Evaluation
• 4.1 Dataset.
• 4.2 Experiment Setup
• 4.3 Results
• 4.4 Threats to Validity
• 5 Conclusion
• References
• Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring
• 1 Introduction
• 2 Preliminaries
• 3 Explainability in OOPPM
• 3.1 Explainability Through Interpretability and Faithfulness
• 3.2 Logit Leaf Model
• 3.3 Generalized Logistic Rule Model
• 4 Experimental Evaluation
• 4.1 Benchmark Models
• 4.2 Event Logs
• 4.3 Implementation
• 4.4 Quantitative Metrics Results
• 5 Conclusion
• References
• SA4PM 2021: 2nd International Workshop on Streaming Analytics for Process Mining
• 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM)
• Organization
• Workshop Chairs
• Program Committee
• Online Prediction of Aggregated Retailer Consumer Behaviour
• 1 Introduction
• 2 Framework
• 2.1 Features
• 2.2 Clustering
• 2.3 Training
• 2.4 Predicting
• 3 Experimental Evaluation
• 3.1 Experimental Setup
• 3.2 Results
• 4 Related Work
• 5 Conclusion and Future Work
• References
• PErrCas: Process Error Cascade Mining in Trace Streams
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 Online Cascade Mining
• 4.1 Outlier Segment-Level Events
• 4.2 Error Cascade Construction
• 4.3 Cascade Patterns
• 5 Evaluation
• 5.1 Synthetic Data
• 5.2 Travel Reimbursement Process
• 6 Conclusion
• References
• Continuous Performance Evaluation for Business Process Outcome Monitoring
• 1 Introduction
• 2 Related Work
• 3 Continuous Prediction Evaluation Framework
• 4 Performance Evaluation Methods
• 4.1 Evaluating Performance Using a Local Timeline
• 4.2 Real-Time Model Performance
• 5 Experimental Analysis and Results
• 6 Conclusions
• References
• PQMI 2021: 6th International Workshop on Process Querying, Manipulation, and Intelligence.
• 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2021)
• Organization
• Workshop Organizers
• Program Committee
• An Event Data Extraction Approach from SAP ERP for Process Mining
• 1 Introduction
• 2 Background
• 2.1 Object-Centric Event Logs
• 2.2 SAP: Entities and Relationships
• 3 Extracting Event Data from SAP ERP: Approach
• 3.1 Building Graphs of Relations
• 3.2 Extracting Object-Centric Event Logs
• 4 Extracting Event Data from SAP ERP: Tool
• 5 Assessment
• 5.1 Building a Graph of Relations
• 5.2 Extracting Object-Centric Event Logs
• 6 Related Work
• 7 Conclusion
• References
• Towards a Natural Language Conversational Interface for Process Mining
• 1 Introduction
• 2 Related Work
• 3 Proposed Method
• 3.1 Pre-processing and Tagging
• 3.2 Semantic Parsing
• 3.3 PM Tool Interface Mapping
• 4 Sample Questions
• 5 Proof of Concept
• 6 Conclusions and Future Work
• References
• On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
• 1 Introduction
• 2 Related Work
• 2.1 Generalization Metric
• 2.2 Adversarial System Variant Approximation
• 3 Notations
• 4 Problem Statement
• 5 Experimental Setup
• 5.1 Sampling Parameter
• 5.2 Variant Log Size
• 5.3 Biased Variant Logs
• 6 Results
• 6.1 Sampling Parameter Results
• 6.2 Variant Log Size Results
• 6.3 Biased Variant Log Results
• 7 Conclusion
• References
• PODS4H 2021: 4th International Workshop on Process-Oriented Data Science for Healthcare
• Fourth International Workshop on Process-Oriented Data Science for Healthcare (PODS4H)
• Organization
• Workshop Chairs
• Program Committee
• Verifying Guideline Compliance in Clinical Treatment Using Multi-perspective Conformance Checking: A Case Study
• 1 Introduction
• 2 Background.
• 3 Research Method.