Business Process Management

Name: Business Process Management | 19th International Conference, BPM 2021, Rome, Italy, September 06-10, 2021, Proceedings
Brand: Springer
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19th International Conference, BPM 2021, Rome, Italy, September 06-10, 2021, Proceedings

Artem Polyvyanyy Moe Thandar Wynn Amy Van Looy Manfred Reichert(Herausgeber*in)

Springer (Verlag)

Erschienen am 27. August 2021

XXV, 474 Seiten

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978-3-030-85469-0 (ISBN)

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Beschreibung

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Inhalt

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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