Full Description
This book constitutes the proceedings of the 29th European Conference on Advances in Databases and Information Systems, ADBIS 2025, which took place in Tampere, Finland, during September 23-26, 2025.
The 14 full papers included in this proceedings book were carefully reviewed and selected from 66 submissions. The book also contains 4 tutorial papers. The selected papers span a large spectrum of topics in the broader field of data management focusing on Query Optimization, Explainable AI, Entity Resolution and Integration, Data and Machine Learning, Spatio-Temporal and Graph Data, and Data Sharing and Synthesis.
Contents
.- Keynote Talk.
.- Data Quality (Assessment) in the Age of AI.
.- Query Optimization.
.- Independence Rules: Analysis of Nine Simple Cardinality Estimators and Their Impact on Plan Quality.
.- Vector Database Benchmarking: A Face Retrieval Use Case.
.- PUL: Pre-load in Software for Caches Wouldn't Always Play Along.
.- Explainable AI.
.- Responsible AI: Training deep learning model efficiently.
.- Exploring local feature influences with Hierarchical Explanation Trees.
.- Towards Trace Variant Explainability.
.- Evaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type Annotation.
.- Entity Resolution & Integration.
.- LSBlock: A Hybrid Blocking System Combining Lexical and Semantic Similarity Search for Record Linkage.
.- Evaluating Quality of Disparate Data Sources: A Discord-Driven Approach.
.- Pasteur: Scaling Privacy-aware Data Synthesis.
.- Generating Semantically Enriched Mobility Data from Travel Diaries.
.- Data & Machine Learning.
.- Learning a Distance for the Stratification of Patients with Amyotrophic Lateral Sclerosis.
.- Designing Efficient and Scalable Substructure Discovery Algorithms for Multilayer Networks.
.- Bitemporal Property Graphs: dealing with both valid and transaction time.
.- Tutorials.
.- Graph Analytics for Bridging Human and Data Sciences.
.- Data Warehousing: The Industrial Perspective.
.- Vector Representations of Multi-Modal Data.
.- Utilizing Quantum Computing to Improve the Quality of Data.
