Cooperative Information Systems: 30th International Conference, CoopIS 2024, Porto, Portugal, November 19–21, 2024, Proceedings(15506 Lecture Notes in Computer Science)

This book constitutes the refereed proceedings of the 30th International Conference on Cooperative Information Systems, CoopIS 2024, held in Porto, Portugal, during November 19-21, 2024. The 16 full papers, 11 short papers and 2 invited papers were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: processes and human-in-the-loop; process analytics and technology; process improvement; knowledge graphs and knowledge engineering; predictive process monitoring; services and cloud; and short papers. 

Table of Contents:
.- Invited Speakers. .- Business Models, Business Processes and Information Systems: A Dynamic Network View. .- Machine Learning and Generative AI in BPM: Recent Developments and Emerging Challenges. .- Processes and Human-in-the-loop. .- Using Eye-Tracking to Detect Search and Inference During Process Model Comprehension. .- Conversationally Actionable Process Model Creation. .- Event Log Extraction for Process Mining Using Large Language Models. .- Process Analytics and Technology. .- All Optimal k-Bounded Alignments Using the FM-Index. .- Unsupervised Anomaly Detection of Prefixes in Event Streams Using Online Autoencoders. .- Autoencoder-Based Detection of Delays, Handovers and Workloads over High-Level Events. .- Process Improvement. .- SwiftMend: An Approach to Detect and Repair Activity Label Quality Issues in Process Event Streams. .- Towards Fairness-Aware Predictive Process Monitoring: Evaluating Bias Mitigation Techniques. .- Knowledge Graphs and Knowledge Engineering. .- A User-Driven Hybrid Neuro-Symbolic Approach for Knowledge Graph Creation from Relational Data. .- Assisted Data Annotation for Business Process Information Extraction from Textual Documents. .- FleX: Interpreting Graph Neural Networks with Subgraph Extraction and Flexible Objective Estimation. .- Predictive Process Monitoring. .- Handling Catastrophic Forgetting: Online Continual Learning for next Activity Prediction. .- A Decomposed Hybrid Approach to Business Process Modeling with LLMs. .- Services and Cloud. .- Self-Organising Approach to Anomaly Mitigation in the Cloud-to-Edge Continuum. .- TALOS: Task Level Autoscaler for Apache Flink. .- Automating Pathway Extraction from Clinical Guidelines: A Conceptual Model, Datasets and Initial Experiments. .- Short Papers. .- IML4DQ: Interactive Machine Learning for Data Quality with Applications in Credit Risk. .- Optimizing B-trees for Memory-Constrained Flash Embedded Devices. .- Predictive Process Approach for Email Response Recommendations. .- Achieving Fairness in Predictive Process Analytics via Adversarial Learning. .- Enhancing Temporal Knowledge Graph Reasoning with Contrastive Learning and Self-Attention Mechanisms. .- Graph Convolution Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs. .- Collaboration Miner: Discovering Collaboration Petri Nets. .- Discovering Order-Inducing Features in Event Knowledge Graphs. .- LabelIT: A Multi-Cloud Resource Label Unification Tool. .- Nala2BPMN: Automating BPMN Model Generation with Large Language Models. .- TeaPie: A Tool for Efficient Annotation of Process Information Extraction Data.