Advances in Databases and Information Systems〈1st ed. 2023〉 : 27th European Conference, ADBIS 2023, Barcelona, Spain, September 4–7, 2023, Proceedings

著者名：Abelló, Alberto (EDT)/Vassiliadis, Panos (EDT)/Romero, Oscar (EDT)/Wrembel, Robert (EDT)

Springer（2023/08/27発売）

• 言語：ENG
• ISBN：9783031429132
• eISBN：9783031429149

Description

This book constitutes the proceedings of the 27th European Conference on Advances in Databases and Information Systems, ADBIS 2023, held in Barcelona, Spain, during September 4–7, 2023.

The 11 full papers presented in this book together with 3 keynotes and tutorials were carefully reviewed and selected from 77 submissions. The papers are organized in the following topical sections: keynote talk and tutorials; query processing and data exploration, data science and fairness and Data and Metadata Quality

Table of Contents

Keynote Talk and Tutorials.- Knowledge Engineering in the Era of Artificial Intelligence.- Causality, Attribution-Scores and Repairs in Databases.- When operations research meets databases.- Query Processing and Data Exploration.- Using a Key-Value Index-Store for Cross-Model Join Queries Over Heterogeneous Data Sources.- Reconstructing Spatiotemporal Data with C-VAEs.- What Happens when Two Multi-Query Optimization Paradigms Combine.- Skyline-Based Temporal Graph Exploration.- Temporal Graph Processing in Modern Memory Hierarchies.- Data Science and Fairness.- Comparing and Improving Active Learning Uncertainty Measures for Transformer Models.- FARMUR: Fair Adversarial Retraining to Mitigate Unfairness in Robustness.- Exploring Heterogeneous Data Graphs Through their Entity Paths.- Managing Linked Nulls in Property Graphs: Tools to Ensure Consistency and Reduce Redundancy.- Assessment of Data Quality in a Data Lakehouse Through Multi-Granularity Data Profiling.- Data Ingestion Validation through Stable Conditional Metrics with Ranking and Filtering.- An Ontology for Representing and Querying Semantic Trajectories in the Maritime Domain.- Extracting Provenance of Machine Learning Experiment Pipeline Artifacts.