Cooperative Information Systems

30th International Conference, CoopIS 2024, Porto, Portugal, November 19-21, 2024, Proceedings
Erscheinungsjahr 2025
ISBN: 978-3-031-81374-0
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 15506, 412 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 645 g

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Comuzzi, Marco

Zhou, Zhangbing

Grigori, Daniela

Sellami, Mohamed

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

.- 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.