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[null,"1.","Record Nr.",null,"UNISA996478872703316"]
[null,null,"Titolo",null,"Advanced information systems engineering : 34th international conference, CAiSE 2022, Leuven, Belgium, June 6-10, 2022 : proceedings / / edited  by Xavier Franch [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-07472-6"]
[null,null,"Descrizione fisica",null,"1 online resource (562 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13295"]
[null,null,"Disciplina",null,"929.605"]
[null,null,"Soggetti",null,"Computer-aided software engineering"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- In Memory of Janis A. Bubenko jr. -- Organization -- Contents -- Process Mining -- Decision Mining with Time Series Data Based on Automatic Feature Generation -- 1 Introduction -- 2 Time Series Based Decision Rules - Analysis -- 3 Approach - EDT-TS -- 3.1 Preprocessing -- 3.2 Feature Generation -- 3.3 Rule Extraction -- 4 Evaluation -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- References -- Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Inferring Graphs from Next Event Predictors -- 4.1 Exhaustive Case Generation -- 4.2 Likelihood Graph Generation -- 4.3 Likelihood Thresholds -- 5 Evaluation -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Evaluation Measures -- 5.4 Results: Synthetic Event Logs -- 5.5 Results: Real-Life Event Log -- 6 Discussion -- 6.1 Limitations -- 6.2 Threats to Validity -- 7 Conclusion -- References -- Bootstrapping Generalization of Process Models Discovered from Event Data -- 1 Introduction -- 2 Background -- 2.1 Systems, Models, Logs, and Their Languages -- 2.2 Process Discovery -- 2.3 Generalization -- 3 Estimating Generalization -- 3.1 Bootstrapping -- 3.2 Bootstrap Framework for Measuring Generalization -- 3.3 Log Sampling -- 3.4 Generalization Measures -- 3.5 Consistency -- 3.6 Example -- 4 Evaluation -- 4.1 Data and Experimentation -- 4.2 Results -- 4.3 Threats to Validity -- 5 Related Work -- 6 Conclusion -- References -- Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning -- 1 Introduction -- 2 Related Work -- "]
[{"$nulls":4},"2.1 Data-Driven Simulation of Business Processes -- 2.2 Generative DL Models of Business Processes -- 3 Hybrid Learning of BPS Models -- 4 Evaluation -- 4.1 Datasets -- 4.2 Evaluation Measures."]
[{"$nulls":4},"4.3 Experiment 1: AS-IS Accuracy of Generated Models -- 4.4 Experiment 2: What-if Analysis -- 5 Conclusion -- References -- Multi-perspective Process Analysis: Mining the Association Between Control Flow and Data Objects -- 1 Introduction -- 2 Background -- 2.1 Preliminaries -- 2.2 Association Rule Mining -- 2.3 Prior Work -- 3 Method -- 3.1 Preparing the Event Log -- 3.2 Encoding the Event Log as a Transaction Table -- 3.3 Mining Association Rules -- 3.4 Analyzing the Rules -- 4 Evaluation -- 4.1 Experiment Setup -- 4.2 Findings -- 4.3 Discussion -- 5 Conclusion -- References -- Sustainable and Explainable Applications -- Towards Greener Applications: Enabling Sustainable-aware Cloud Native Applications Design -- 1 Introduction -- 2 State of the Art -- 3 Motivating Scenario -- 4 Sustainable Application Design Process -- 5 Sustainable Workflow Design with SADP -- 6 Validation -- 6.1 Designing Sustainable Applications -- 6.2 Designing Sustainable Workflows -- 6.3 Feasibility and Open Challenges -- 7 Conclusion -- References -- Towards Explainable Artificial Intelligence in Financial Fraud Detection: Using Shapley Additive Explanations to Explore Feature Importance -- 1 Introduction -- 2 Artificial Intelligence in Fraud Detection and Its Need for Transparency -- 2.1 Financial Fraud Detection -- 2.2 Explainable Artificial Intelligence with Shapley Additive Explanations -- 3 Information Systems Engineering Approach -- 4 Results: An Explainable Financial Fraud Detection Pipeline -- 4.1 Developing Machine Learning Models for Financial Fraud Detection -- 4.2 Explaining Machine Learning Models by Shapley Additive Explanations -- 5 Conclusion -- References -- Tools and Methods to Support Research and Design -- Systematic Literature Review Search Query Refinement Pipeline: Incremental Enrichment and Adaptation -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"3 Incremental Query Building and Refining Pipeline -- 3.1 Initial Query Builder -- 3.2 Query Enrichment -- 3.3 Query Adaptation -- 4 Evaluation -- 4.1 Methods -- 4.2 Results -- 5 Conclusion and Future Work -- References -- A Model-Driven Approach for Systematic Reproducibility and Replicability of Data Science Projects -- 1 Introduction -- 2 Model-Driven Data Science Projects R&amp -- R -- 2.1 Overview -- 2.2 Metamodels -- 2.3 Libraries -- 3 Illustrative Example -- 3.1 Conceptual Model -- 3.2 Operational Model -- 4 Evaluation -- 5 Related Works -- 6 Discussion and Future Work -- 7 Conclusions -- References -- The Aircraft and Its Manufacturing System: From Early Requirements to Global Design -- 1 Introduction -- 2 Industrial Case Study -- 3 Identify Goals and Objectives -- 3.1 Model the Goal Oriented Requirements -- 3.2 Application -- 4 Support the Optimal Overall System Design -- 4.1 Support the Design of an Optimal Solution -- 4.2 Application: Conceptual Model -- 4.3 Application: Assembly Line Design Optimization Tool -- 5 Lessons Learned -- 6 Related Work -- 6.1 Optimal Design of a Product and Its Production System -- 6.2 Trade-off Between Design Choices in Other Fields -- 6.3 The Relation with Systems of Systems -- 6.4 The Use of Multi-modelling Approaches -- 7 Conclusion and Future Work -- References -- Process Modeling -- Causal Reasoning over Control-Flow Decisions in Process Models -- 1 Introduction -- 2 Motivating Example -- 3 Related Work -- 4 Preliminaries -- 5 Our Method -- 5.1 Upper-Bound Causal Graphs -- 5.2 Choice Data -- 5.3 Causal Discovery -- 6 Evaluation -- 7 Conclusion -- References -- Crop Harvest Forecast via Agronomy-Informed Process Modelling and Predictive Monitoring -- 1 "]
[{"$nulls":4},"Introduction -- 2 Background and Related Work -- 3 Approach -- 3.1 Data Fusion -- 3.2 AIPA Predictive Model -- 4 Case Study -- 4.1 Context."]
[{"$nulls":4},"4.2 Application of the Approach -- 4.3 Results Analysis and Discussion -- 5 Discussion -- 6 Conclusion -- References -- Guiding Knowledge Workers Under Dynamic Contexts -- 1 Introduction -- 2 Running Example -- 3 Guiding Knowledge Workers with Task Prioritization Method -- 4 Evaluation -- 4.1 Case Study -- 4.2 Evaluation Results -- 5 Related Work and Discussion -- 6 Conclusion -- References -- Natural Language Processing Techniques in IS Engineering -- Context Knowledge-Aware Recognition of Composite Intents in Task-Oriented Human-Bot Conversations -- 1 Introduction -- 2 Scenario and Architecture -- 2.1 Scenario -- 2.2 Architecture -- 3 Extended Context Knowledge Service -- 3.1 Context Knowledge Model -- 3.2 CK Services -- 4 Complex Intent Recognition -- 4.1 Functions -- 4.2 Complex Intent Recognition Rules -- 5 Experiments -- 5.1 Methods -- 5.2 Results -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Crowdsourcing Syntactically Diverse Paraphrases with Diversity-Aware Prompts and Workflows -- 1 Introduction -- 2 Related Work -- 3 Crowdsourcing Syntactically Diverse Paraphrases -- 3.1 Paraphrase Generation Workflow -- 3.2 Pattern Representation and Selection -- 3.3 Paraphrase Generation Prompts -- 4 Experiment Design -- 5 Results -- 5.1 Impact on the Relevance of Crowdsourced Paraphrases -- 5.2 Guiding the Crowd Towards Syntactic Variations -- 5.3 Impact on Task Effort -- 6 Discussion and Conclusion -- References -- A Subject-aware Attention Hierarchical Tagger for Joint Entity and Relation Extraction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Tagging Scheme -- 3.2 Subject-aware Attention Hierarchical Tagger -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Baselines and Evaluation Metrics -- 4.4 Experimental Results and Analyses -- 5 Conclusion -- References -- Process Monitoring and Simulation."]
[{"$nulls":4},"Estimating Activity Start Timestamps in the Presence of Waiting Times via Process Simulation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Technique -- 5 Implementation and Experiments -- 5.1 Case Study of the Process for Student Credential Recognition -- 5.2 Purchase Process Case Study -- 6 Conclusion -- References -- Updating Prediction Models for Predictive Process Monitoring -- 1 Introduction -- 2 Predictive Process Monitoring -- 3 Updating Predictive Models -- 4 Experimental Evaluation -- 4.1 Experiment Setup -- 4.2 Event Logs -- 4.3 Results -- 5 Related Work -- 6 Conclusions -- References -- Multi-model Monitoring Framework for Hybrid Process Specifications -- 1 Introduction -- 2 Example Scenario -- 3 Process Components -- 4 Multi-model Monitoring Framework for Hybrid Process Specifications -- 4.1 Elicitation Phase -- 4.2 Preparation Phase -- 4.3 Monitoring Phase -- 5 Automata-Based Monitoring -- 5.1 Monitoring Semantics -- 5.2 Monitoring Automaton -- 5.3 Event Processing -- 6 Preliminary Experiments -- 7 Related Work -- 8 Conclusion -- References -- Graph and Network Models -- Mining Valuable Collaborations from Event Data Using the Recency-Frequency-Monetary Principle -- 1 Introduction -- 2 Related Work -- 2.1 Organizational Network Analysis -- 2.2 Process Mining -- 2.3 Developer Social Networks -- 2.4 Recency-Frequency-Monetary Model -- 3 Algorithm Design -- 3.1 Input Requirements -- 3.2 Mining the Collaboration Relationships -- 3.3 RFM Values for a Relationship -- 3.4 Constructing the Work Sessions -- 3.5 RFM Values for a Resource -- 4 Demonstration -- 5 Conclusions, Limitations, and Future Work -- References -- Querying Temporal Property Graphs -- 1 Introduction -- "]
[{"$nulls":4},"2 Related Work -- 3 Proposition -- 3.1 Model -- 3.2 Operators -- 3.3 Mapping from Temporal Graph Operators to a Property Graph Operators."]
[{"$nulls":4},"4 Experimental Evaluation."]