Database and Expert Systems Applications

36th International Conference, DEXA 2025, Bangkok, Thailand, August 25-27, 2025, Proceedings, Part I
Erscheinungsjahr 2025
ISBN: 978-3-032-02048-2
Verlag: Springer

Buch, Englisch, 383 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 622 g

Reihe: Lecture Notes in Computer Science

The two-volume set LNCS 16046-16047 constitutes the proceedings of the 36th International Conference on Database and Expert Systems Applications, DEXA 2025, held in Bangkok, Thailand, in August 25–27, 2025.

The 35 full and 22 short papers presented in this set together with 3 invited talks were carefully reviewed and selected from 123 submissions. They were organized in topical sections as follows:

Part I: Industrial Keynote; Invited Talks; Large Language Models; Data Quality; Machine Learning /Artificial Intelligence Applications; Classification Techniques.

Part II: Image Processing, Analytics, and Vision Systems; Recommender Techniques; Data Integration; Optimisation Methods; Graph Applications; Analytics; Security/Privacy; Benchmarks and Surveys.

Zielgruppe

Research

Autoren/Hrsg.

Wrembel, Robert

Kotsis, Gabriele

Tjoa, A Min

Khalil, Ismail

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

.- Industrial Keynote.

.- From Data Silos to Data Mesh: A Case Study in Financial Data Architecture.

.- Invited Talk.

.- Blending Contextual Data with Heterogeneous Time Dimensions for Improved Time Series Analysis.

.- A Hybrid Data Model to Support Transportation Analytics of Emergency Service Vehicles.

.- Large Language Models.

.- Automated Archival Descriptions with Federated Intelligence of LLMs.

.- Entropy-Guided Probing for Predicting LLM Hallucinations with Knowledge Graph Features.

.- Towards Automating RDF Extraction for Archaeological Knowledge Graphs with LLMs.

.- Ontology-Based Forest Fire Management using Complex Event Processing and Large Language Models.

.- Table Annotation Utilizing Large Language Model and Knowledge Graph.

.- Improving Software Security Through a LLM-Based Vulnerability Detection Model.

.- SysResolve: Study on In-Context LLM Generation of Resolution Scripts.

.- Data Quality.

.- A Novel Unsupervised Anomaly Detection Method Based on TCN-LSTM-CMA Autoencoder.

.- Behaviour modelling and Wayfinding Error Detection in Low Mountain Hiking.

.- Explainable Time Series Anomaly Detection by Dynamic Mode Decomposition.

.- Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study.

.- AI-Driven Semantic Data Quality Assessment and Scoring for Relational Databases.

.- Network Anomaly Detection Using Gramian Angular Field Transformation and Vision Transformer.

.- Machine Learning /Artificial Intelligence Applications.

.- Identifying Multimodal Sarcasm Based on Incongruous Knowledge Capturing and Contrastive Learning.

.- Ensemble ToT and Its Application to Automatic Grading.

.- Improving Prompt-based Learning Framework for Mental Health Aspect Detection from Social Media.

.- DInos: A Deep Reinforcement Learning Approach to Generalizable Autoscaling in Stateless Cloud Applications.

.- Influential Slot and Tag Selection in Billboard Advertisement.

.- Speech-scenario Generation based on the Philosophy of Prominent Leader within the Small Community.

.- VarCGAN: Variational Cyclic Generative Adversarial Network for Music Genre Style Transfer.

.- Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions.

.- A Hybrid Approach to estimating AI Carbon Emissions.

.- Optimal Information Retrieval System in E-Learning Using Optimization-Driven Bidirectional Long Short-Term Memory.

.- A Data Product Classification by Technical and Machine Learning Aspects.

.- Classification Techniques.

.- Discovering Voting Power for Ensemble Methods.

.- Classifying Public and Private Documents Using Context-Based Predictions.