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[null,null,"Titolo",null,"Business Process Management : 19th International Conference, BPM 2021, Rome, Italy, September 06–10, 2021, Proceedings / / edited by Artem Polyvyanyy, Moe Thandar Wynn, Amy Van Looy, Manfred Reichert"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-85469-8"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (480 pages)"]
[null,null,"Collana",null,"Information Systems and Applications, incl. Internet/Web, and HCI, , 2946-1642 ; ; 12875"]
[null,null,"Disciplina",null,"658.4038011"]
[null,null,"Soggetti",null,"Information storage and retrieval systems"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Electronic commerce"]
[{"$nulls":4},"Information technology - Management"]
[{"$nulls":4},"Information Storage and Retrieval"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"e-Commerce and e-Business"]
[{"$nulls":4},"Computer Application in Administrative Data Processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Keynote Abstracts -- What Have the Romans Ever Done for Us? The Ancient Antecedents of Business Process Management -- Artificial Intelligence-based Declarative Process Synthesis for BPM -- Contents -- Keynote Paper -- Process Automation and Process Mining in Manufacturing -- 1 Introduction -- 2 Automating Legacy vs. Automating Greenfield Scenarios in Manufacturing -- 2.1 Automating Legacy Scenarios -- 2.2 Automating Greenfield Scenarios -- 3 The Human Aspect in Process Automation -- 4 Process Mining and Automation: Are They Twins? -- 5 Discussion and Outlook -- References -- Tutorials -- Cognitive Effectiveness of Representations for Process Mining -- 1 Introduction -- 2 Visual Representations for Process Mining -- 3 Cognitive Effectiveness of Process Mining Outputs -- 4 Evaluating Process Mining from a Cognitive Angle -- 5 Conclusion -- References -- RuM: Declarative Process Mining, Distilled -- 1 Introduction -- 2 Declarative Process Mining with RuM -- 3 Considerations About Declarative Process Mining "]
[{"$nulls":4},"-- 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."]
[{"$nulls":4},"Weighing the Pros and Cons: Process Discovery with Negative Examples -- 1 Introduction -- 2 Process Notations and Unary Discovery -- 3 Process Discovery as Binary Classification -- 4 Rejection Miners -- 5 Cases with Negative Examples -- 5.1 DCR Solutions: Test-Driven Modelling -- 5.2 Dreyer Foundation: Process Engineering -- 6 Experimental Results -- 6.1 Results -- 7 Conclusion -- References -- A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties -- 1 Introduction -- 2 Basic Terminology and Problem Illustration -- 3 Debugging of Process Discovery Pipelines -- 3.1 Sampling the Pipeline -- 3.2 Measuring the Property Consistency for a Single Execution -- 3.3 Analyzing the Property Consistency for the Pipeline -- 4 Experiment -- 4.1 Experimental Design -- 4.2 Results -- 5 Related Work -- 6 Conclusion -- References -- Extracting Decision Models from Textual Descriptions of Processes -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Decision Model and Notation (DMN) -- 3.2 Natural Language Processing and Annotation -- 3.3 TRegex -- 4 Approach -- 4.1 Decision Requirement Level -- 4.2 Decision Logic Level -- 4.3 Simple Expression Language -- 4.4 Decision Model Extraction Without Requirement Level -- 4.5 Discussion -- 5 Tool Support and Experiments -- 6 Conclusions and Future Work -- References -- Predictive Process Monitoring -- Robust and Generalizable Predictive Models for Business Processes -- 1 Introduction -- 2 Background -- 2.1 Event Logs, Traces, and Sequences -- 2.2 Predictive Monitoring Tasks -- 2.3 Neural Networks and Invariant Risk Minimization -- 2.4 Sequence Prediction Neural Networks -- 3 Related Work -- 3.1 Predictive Models for Business Processes -- 3.2 Generalization Approaches -- 4 Our Approach -- 4.1 Data Preprocessing -- 4.2 RoGen Model Architecture and Training Workflow."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Uncertainty -- 4.4 Base Case -- 5 Results -- 5.1 Overall Performance -- 5.2 Uncertainty Estimates -- 5.3 Computation Time -- 6 Applications of Uncertainty -- 7 Conclusion and Future Work -- References -- Data- and Time-awareness in BPM -- Zoom and Enhance: Action Refinement via Subprocesses in Timed Declarative Processes -- 1 Introduction -- 2 Timed DCR Graphs -- 3 Timed DCR Graphs with Subprocesses -- 4 Refinement via Subprocess Expansion -- 5 Conclusions, Related and Future Work -- References -- Delta-BPMN: A Concrete Language and Verifier for Data-Aware BPMN -- 1 Introduction -- 2 Requirement Analysis and Related Work -- 3 The PDMML Language -- 3.1 Sources of Data and Their Definition."]
[{"$nulls":4},"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."]
[{"$nulls":4},"5 Using the Discounted Cost Function in an A*-Based Algorithm for Discounted Alignments -- 5.1 Algorithm for Computing Optimal Discounted Alignments -- 5.2 A Heuristic for Reducing the Search Space of the Algorithm -- 6 Experiments: Discounted Alignments as a Heuristic for Approximating Classical Alignments -- 6.1 Comparison with Respect to Baselines -- 6.2 Influence of the Discount Parameter  on the Quality and Runtime -- 7 Conclusion -- References -- Blockchain and Robotic Process Automation -- Task Clustering Method Using User Interaction Logs to Plan RPA Introduction -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 UI Log -- 3.2 Segmentation -- 3.3 Clustering -- 4 Evaluation -- 4.1 Datasets -- 4.2 Evaluation Method -- 4.3 Result -- 4.4 Limitations of Our Approach -- 4.5 Visualization Result -- 5 Conclusion -- References -- From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots -- 1 Introduction -- 2 Theoretical Background -- 2.1 Symbolic Robotic Process Automation -- 2.2 Intelligent Robotic Process Automation -- 3 Research Design -- 3.1 Overview -- 3.2 Literature Review -- 3.3 Expert Study on Intelligent RPA in Current and Future Business Practice -- 4 Proposed Challenges "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Process Mining on Blockchain Data: A Case Study of Augur."]
[null,null,null,"Sommario/riassunto","This volume constitutes the refereed proceedings of the 19th International Conference on Business Process Management, BPM 2021, held in Rome, Italy, in September 2021. 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."]