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Cooperative Information Systems: 30th International Conference, CoopIS 2024, Porto, Portugal, November 1921, 2024, Proceedings [Mīkstie vāki]

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  • Formāts: Paperback / softback, 412 pages, height x width: 235x155 mm, 136 Illustrations, color; 26 Illustrations, black and white; XVI, 412 p. 162 illus., 136 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15506
  • Izdošanas datums: 14-Feb-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303181374X
  • ISBN-13: 9783031813740
  • Mīkstie vāki
  • Cena: 61,19 €*
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  • Formāts: Paperback / softback, 412 pages, height x width: 235x155 mm, 136 Illustrations, color; 26 Illustrations, black and white; XVI, 412 p. 162 illus., 136 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15506
  • Izdošanas datums: 14-Feb-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303181374X
  • ISBN-13: 9783031813740

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. 

.- 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.