
Business Process Management
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The 23 full papers, one keynote paper, and 4 tutorial papers presented in this volume were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections named: foundations, engineering, and management.
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Content
- Intro
- Preface
- Organization
- Keynote Abstracts
- What Have the Romans Ever Done for Us? The Ancient Antecedents of Business Process Management
- Artificial Intelligence-based Declarative Process Synthesis for BPM
- Contents
- Keynote Paper
- Process Automation and Process Mining in Manufacturing
- 1 Introduction
- 2 Automating Legacy vs. Automating Greenfield Scenarios in Manufacturing
- 2.1 Automating Legacy Scenarios
- 2.2 Automating Greenfield Scenarios
- 3 The Human Aspect in Process Automation
- 4 Process Mining and Automation: Are They Twins?
- 5 Discussion and Outlook
- References
- Tutorials
- Cognitive Effectiveness of Representations for Process Mining
- 1 Introduction
- 2 Visual Representations for Process Mining
- 3 Cognitive Effectiveness of Process Mining Outputs
- 4 Evaluating Process Mining from a Cognitive Angle
- 5 Conclusion
- References
- RuM: Declarative Process Mining, Distilled
- 1 Introduction
- 2 Declarative Process Mining with RuM
- 3 Considerations About Declarative Process Mining
- 4 Research Opportunities
- References
- Applications of Automated Planning for Business Process Management
- 1 Why Automated Planning for Business Processes?
- 2 Automated Planning for BPM
- 2.1 Automated Generation of Process Models
- 2.2 Trace Alignment
- 2.3 Process Adaptation
- 2.4 Interpretability and Authoring Tools
- 3 Conclusions
- References
- Artifact-Driven Process Monitoring: A Viable Solution to Continuously and Autonomously Monitor Business Processes
- 1 Introduction to Process Monitoring
- 1.1 Challenges in Process Monitoring
- 2 Artifact-Driven Monitoring in a Nutshell
- 2.1 E-GSM Modeling Language
- 2.2 From BPMN to E-GSM
- 2.3 SMARTifact: An Artifact-Driven Monitoring Platform
- References
- Process Discovery
- Weighing the Pros and Cons: Process Discovery with Negative Examples
- 1 Introduction
- 2 Process Notations and Unary Discovery
- 3 Process Discovery as Binary Classification
- 4 Rejection Miners
- 5 Cases with Negative Examples
- 5.1 DCR Solutions: Test-Driven Modelling
- 5.2 Dreyer Foundation: Process Engineering
- 6 Experimental Results
- 6.1 Results
- 7 Conclusion
- References
- A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties
- 1 Introduction
- 2 Basic Terminology and Problem Illustration
- 3 Debugging of Process Discovery Pipelines
- 3.1 Sampling the Pipeline
- 3.2 Measuring the Property Consistency for a Single Execution
- 3.3 Analyzing the Property Consistency for the Pipeline
- 4 Experiment
- 4.1 Experimental Design
- 4.2 Results
- 5 Related Work
- 6 Conclusion
- References
- Extracting Decision Models from Textual Descriptions of Processes
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 3.1 Decision Model and Notation (DMN)
- 3.2 Natural Language Processing and Annotation
- 3.3 TRegex
- 4 Approach
- 4.1 Decision Requirement Level
- 4.2 Decision Logic Level
- 4.3 Simple Expression Language
- 4.4 Decision Model Extraction Without Requirement Level
- 4.5 Discussion
- 5 Tool Support and Experiments
- 6 Conclusions and Future Work
- References
- Predictive Process Monitoring
- Robust and Generalizable Predictive Models for Business Processes
- 1 Introduction
- 2 Background
- 2.1 Event Logs, Traces, and Sequences
- 2.2 Predictive Monitoring Tasks
- 2.3 Neural Networks and Invariant Risk Minimization
- 2.4 Sequence Prediction Neural Networks
- 3 Related Work
- 3.1 Predictive Models for Business Processes
- 3.2 Generalization Approaches
- 4 Our Approach
- 4.1 Data Preprocessing
- 4.2 RoGen Model Architecture and Training Workflow
- 5 Evaluation
- 5.1 Experimental Setup
- 5.2 Results
- 6 Conclusion and Future Work
- References
- Incremental Predictive Process Monitoring: The Next Activity Case
- 1 Introduction
- 2 Related Work
- 2.1 Predictive Process Monitoring
- 2.2 Concept Drift Detection
- 2.3 Incremental Learning Algorithms
- 2.4 Incremental Predictive Process Monitoring
- 3 Update Strategies
- 3.1 Data Selection
- 3.2 Update Existing Methods
- 4 Reference Model: Single Dense Layer (SDL)
- 5 Experiments
- 5.1 Dataset Selection
- 5.2 Baseline Comparison
- 5.3 Update Strategy
- 5.4 Runtime Results
- 5.5 Overall Results
- 6 Conclusion
- References
- Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes
- 1 Introduction
- 2 Remaining Time Prediction: Definition and Related Work
- 3 Estimating Uncertainty
- 3.1 Estimating Epistemic Uncertainty with Bayesian Neural Networks
- 3.2 Estimating Heteroscedastic Aleatoric Uncertainty
- 3.3 LSTM Vs. CNN
- 3.4 Objectives
- 4 Experimental Setup
- 4.1 Datasets
- 4.2 Preprocessing
- 4.3 Estimating the Epistemic, Aleatoric and Total Uncertainty
- 4.4 Base Case
- 5 Results
- 5.1 Overall Performance
- 5.2 Uncertainty Estimates
- 5.3 Computation Time
- 6 Applications of Uncertainty
- 7 Conclusion and Future Work
- References
- Data- and Time-awareness in BPM
- Zoom and Enhance: Action Refinement via Subprocesses in Timed Declarative Processes
- 1 Introduction
- 2 Timed DCR Graphs
- 3 Timed DCR Graphs with Subprocesses
- 4 Refinement via Subprocess Expansion
- 5 Conclusions, Related and Future Work
- References
- Delta-BPMN: A Concrete Language and Verifier for Data-Aware BPMN
- 1 Introduction
- 2 Requirement Analysis and Related Work
- 3 The PDMML Language
- 3.1 Sources of Data and Their Definition
- 3.2 The Process Component of delta-BPMN
- 3.3 Inspecting and Manipulating Data with PDMML
- 3.4 Guards for Conditional Flows
- 4 delta-BPMN in Action
- 4.1 Modeling delta-BPMN Processes with Camunda
- 4.2 Encoding delta-BPMN Camunda Processes in MCMT
- 5 Conclusions
- References
- A Real-Time Method for Detecting Temporary Process Variants in Event Log Data
- 1 Introduction
- 2 Related Work
- 3 Background
- 3.1 Non-Euclidean Relational Fuzzy C-Means (NERFCM)
- 3.2 Correlation Cluster Validity (CCV)
- 4 Proposed Method
- 4.1 Input Parameters
- 4.2 Overview of Proposed Method
- 4.3 Steps
- 5 Method Evaluation
- 5.1 Event Logs
- 5.2 Experiment Setup
- 5.3 Results
- 6 Conclusions
- References
- Conformance Checking
- CoCoMoT: Conformance Checking of Multi-perspective Processes via SMT
- 1 Introduction
- 2 Preliminaries
- 2.1 Data Petri Nets
- 2.2 Event Logs and Alignments
- 2.3 Satisfiability Modulo Theories (SMT)
- 3 Conformance Checking via SMT
- 3.1 Distance-Based Cost Function
- 3.2 Encoding
- 3.3 Complexity
- 4 Trace Clustering
- 5 Implementation and Experiments
- 6 Discussion
- 7 Conclusions
- References
- Aligning Data-Aware Declarative Process Models and Event Logs
- 1 Introduction
- 2 Related Work
- 3 Preliminary Definitions
- 3.1 Event Logs
- 3.2 Data-Aware Declare
- 3.3 Automated Planning
- 4 Working Assumptions
- 5 Data-Aware Declarative Conformance Checking as Planning
- 5.1 -encoding for Conformance Checking
- 5.2 Automaton Manipulation for Trace Alignment
- 5.3 Encoding in PDDL
- 5.4 Trace Repair
- 6 Experiments
- 7 Conclusions
- References
- A Discounted Cost Function for Fast Alignments of Business Processes
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 4 Discounted Cost Function and Properties
- 5 Using the Discounted Cost Function in an A*-Based Algorithm for Discounted Alignments
- 5.1 Algorithm for Computing Optimal Discounted Alignments
- 5.2 A Heuristic for Reducing the Search Space of the Algorithm
- 6 Experiments: Discounted Alignments as a Heuristic for Approximating Classical Alignments
- 6.1 Comparison with Respect to Baselines
- 6.2 Influence of the Discount Parameter on the Quality and Runtime
- 7 Conclusion
- References
- Blockchain and Robotic Process Automation
- Task Clustering Method Using User Interaction Logs to Plan RPA Introduction
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 UI Log
- 3.2 Segmentation
- 3.3 Clustering
- 4 Evaluation
- 4.1 Datasets
- 4.2 Evaluation Method
- 4.3 Result
- 4.4 Limitations of Our Approach
- 4.5 Visualization Result
- 5 Conclusion
- References
- From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots
- 1 Introduction
- 2 Theoretical Background
- 2.1 Symbolic Robotic Process Automation
- 2.2 Intelligent Robotic Process Automation
- 3 Research Design
- 3.1 Overview
- 3.2 Literature Review
- 3.3 Expert Study on Intelligent RPA in Current and Future Business Practice
- 4 Proposed Challenges for RPA, AI, and Intelligent RPA
- 4.1 RPA Challenges Impacting Intelligent RPA
- 4.2 AI Challenges Impacting Intelligent RPA
- 4.3 Derivation of Challenges at the Intersection of RPA and AI
- 5 Consolidated Challenges Impacting Intelligent RPA
- 5.1 Overview of Challenges
- 5.2 Organizational and Socio-Technical Challenges During Build-Time
- 5.3 Technical Implementation Challenges During Build-Time
- 5.4 Technical Implementation Challenges During Run-Time
- 5.5 Organizational and Socio-Technical Challenges During Run-Time
- 6 Discussion
- 7 Conclusion
- References
- Process Mining on Blockchain Data: A Case Study of Augur
- 1 Introduction
- 2 The Case of Augur
- 3 Data Extraction and Pre-processing
- 4 Process Mining Analysis and Results
- 4.1 Exploring the Event Data
- 4.2 Process Discovery
- 4.3 Conformance Checking and Unusual Cases
- 4.4 Performance Analysis
- 5 Discussion
- 6 Conclusion and Future Work
- References
- Process and Resource Analytics
- Multivariate Business Process Representation Learning Utilizing Gramian Angular Fields and Convolutional Neural Networks
- 1 Introduction
- 2 Foundations
- 2.1 Business Process Event Log and Perspectives
- 2.2 Business Process Data and Representation Learning
- 3 Related Work
- 4 Multi-Perspective Process Network (MPPN)
- 4.1 Graphical Representation of Event Log Data
- 4.2 Architecture
- 4.3 Training Method
- 4.4 Implementation Details
- 5 Evaluation
- 5.1 Representation Visualization and Retrieval
- 5.2 Next Step and Outcome Prediction
- 6 Conclusion
- References
- Seeing the Forest for the Trees: Group-Oriented Workforce Analytics
- 1 Introduction
- 2 Resource Group Analysis: Theory and Related Work
- 3 Work Profile of Resource Groups
- 4 Identifying and Analyzing Work Profiles
- 4.1 Identifying Work Profiles
- 4.2 Analyzing Work Profiles
- 5 Evaluation
- 5.1 Design of Experiments
- 5.2 Group-Level Analysis
- 5.3 Within-Group Analysis
- 6 Discussion, Implications, and Conclusion
- References
- A Case Study of Inconsistency in Process Mining Use: Implications for the Theory of Effective Use
- 1 Introduction
- 2 Related Work
- 2.1 Information Systems Use
- 2.2 Business Intelligence Use
- 3 Grounded Theory Case Study
- 3.1 Case Organization
- 3.2 Data Analysis
- 4 Findings
- 4.1 Interrelationship Between Inconsistency in Data and Inconsistency in Information (R1)
- 4.2 Relationships Between Inconsistency in Meaning and Actionable Insights (R2)
- 4.3 Interrelationships Between Inconsistency in Meaning and Inconsistency in Content (R3)
- 4.4 Relationships Between Inconsistency in Content and Actionable Insights (R4)
- 4.5 Interrelationships Between Inconsistency in Content and Inconsistency in Place (R5)
- 4.6 Relationship Between Inconsistency in Place and Actionable Insights (R6)
- 4.7 Summary of Findings
- 5 Discussion
- 5.1 Importance of Inconsistency-In-Use for Process Mining
- 5.2 Extending the Theory of Effective Use
- 6 Conclusion
- References
- Concept Drift and Anomaly Detection from Event Logs
- A Robust and Accurate Approach to Detect Process Drifts from Event Streams
- 1 Introduction
- 2 Background
- 2.1 Detecting Process Drifts by Statistical Tests
- 2.2 Other Process Drift Detection Methods
- 3 Preliminaries
- 4 Concept Drift Detection
- 4.1 Selection of Features
- 4.2 Validation of Candidate Drift Points
- 4.3 Bidirectional Searches
- 4.4 The Framework of the Proposed Method
- 5 Evaluation on Synthetic Data
- 5.1 Evaluation Design
- 5.2 Evaluation on Different Parameter Settings
- 5.3 Comparing with the Baseline on Different Change Patterns
- 5.4 Execution Time
- 6 Evaluation on Real-Life Data
- 7 Conclusion
- References
- A Framework for Explainable Concept Drift Detection in Process Mining
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 4 Generic Framework for Explainable Concept Drift
- 4.1 Time Series Construction
- 4.2 Change Point Detection
- 4.3 Cause-Effect Analysis
- 5 Evaluation
- 5.1 Implementation
- 5.2 Experiments
- 5.3 Synthetic Insurance Event Log
- 5.4 Case Study
- 5.5 Comparison
- 6 Conclusion
- References
- Graph Autoencoders for Business Process Anomaly Detection
- 1 Introduction
- 2 Background
- 2.1 Autoencoders and Anomaly Detection
- 2.2 Feature Encoding for Business Process Event Logs
- 2.3 Graph Neural Networks
- 3 Method
- 3.1 Graph Construction on the Process Event Log
- 3.2 Encoding by the Graph Autoencoder with ECC
- 4 Experiments
- 4.1 Simulated Anomalous Data Sets
- 4.2 Experiment Settings and Results
- 4.3 Anomaly Example and Diagnostic Information
- 5 Conclusion
- References
- Digital Innovation and Process Improvement
- Drivers and Barriers of the Digital Innovation Process - Case Study Insights from a German Public University
- 1 Introduction
- 2 Background
- 2.1 Digital Innovation
- 2.2 Business Process Management
- 2.3 Convergence of DI and BPM
- 3 Method
- 3.1 Research Design and Case Selection
- 3.2 Data Collection and Analysis
- 4 Case Description
- 5 Results
- 5.1 Drivers and Barriers of the Digital Innovation Process
- 5.2 Overall Findings and Practice Recommendations
- 6 Discussion, Limitations, and Conclusion
- References
- A Stakeholder Engagement Model for Process Improvement Initiatives
- 1 Introduction
- 2 Background
- 3 Case Study Design
- 4 Findings
- 4.1 Micro Level Findings
- 4.2 Exo Level Findings
- 4.3 Macro Level Findings
- 4.4 Chrono Level Findings
- 5 Summary Discussions
- 5.1 Theoretical Implications
- 5.2 Practical Implications
- 6 Conclusion
- References
- Author Index
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