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

Bibliographic Details
Main Author: Munoz-Gama, Jorge.
Other Authors: Lu, Xixi.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2022.
Edition:1st ed.
Series:Lecture Notes in Business Information Processing Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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