Advances in Databases and Information Systems 28th European Conference, ADBIS 2024, Bayonne, France, August 28–31, 2024, Proceedings /
This volume LNCS 14918 constitutes the refereed proceedings of 28th European Conference, ADBIS 2024, held in Bayonne, France, during August 28-31, 2024. The 15 full papers presented were carefully reviewed and selected from 43 submissions. The conference focuses on Algebra, Models, Schemata, Discove...
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| Autres auteurs: | , , , |
| Format: | Électronique eBook |
| Langue: | anglais |
| Publié: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Édition: | 1st ed. 2024. |
| Collection: | Lecture Notes in Computer Science,
14918 |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-031-70626-4 |
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Table des matières:
- Algebra, Models, Schemata
- JSON Model a Lightweight Featureful DSL for JSON
- A Data Model and Predicate Logic for Trajectory Data
- Evaluating a Temporal Relational Algebra supporting Preferences in Temporal Relational Databases
- PG FD Mapping Functional Dependencies to the Future Property Graph Schema Standard
- Discovery and Data Analysis
- Generating SPARQL Queries for Data Discovery
- Algorithms and Optimization
- A Recursive Approach for Maximal (Δ,γ) Clique Enumeration in Temporal Networks
- Influential Billboard Slot Selection under Zonal Influence Constraint
- Amethyst A Generalized on the fly De Recompression Framework to Accelerate Data intensive Integer Operations on GPUs
- Access Methods and Query Processing
- GXJoin Generalized Cell Transformations for Explainable Joinability
- Density Based Learned Spatial Index for Clustered Data
- Advanced Architectures
- Coordination Free Parallel and Replicated Datalog Streams
- On the fly Data Distribution to Accelerate Query Processing in Heterogeneous
- Enhancing Machine Learning Capabilities in Data Lakes with AutoML and LLMs
- Machine Learning
- BBQ Tree A Decision Tree with Boolean and Quantum Logic Decisions
- Large Language Models
- Using LLMs for the Extraction and Normalization of Product Attribute Values.