Advanced Information Systems Engineering 34th International Conference, CAiSE 2022, Leuven, Belgium, June 6-10, 2022, Proceedings

This book constitutes the refereed proceedings of the 34th International Conference on Advanced Information Systems Engineering, CAiSE 2022, which was held in Leuven, Belgium, during June 6-10, 2022.The 31 full papers included in these proceedings were selected from 203 submissions. They were organi...

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Bibliographic Details
Main Authors Conference on Advanced Information Systems Engineering, Franch, Xavier
Format eBook Book
LanguageEnglish
Published Cham Springer Nature 2022
Springer
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesLecture Notes in Computer Science
Subjects
Computer-aided software engineering
Data processing Computer science
Software engineering
Software engineering > Congresses
Systems engineering
Systems engineering > Congresses
Online AccessGet full text

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Table of Contents:
  • 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 -- 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
  • 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 -- 2 Related Work -- 3 Proposition -- 3.1 Model -- 3.2 Operators -- 3.3 Mapping from Temporal Graph Operators to a Property Graph Operators
  • 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
  • 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
  • 4 Experimental Evaluation
  • 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 Introduction -- 2 Background and Related Work -- 3 Approach -- 3.1 Data Fusion -- 3.2 AIPA Predictive Model -- 4 Case Study -- 4.1 Context