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Advances in Databases and Information Systems

28th European Conference, ADBIS 2024, Bayonne, France, August 28–31, 2024, Proceedings

AvJoe Tekli,Johann Gamper

Häftad, Engelska, 2024

Del i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-10-03
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:231
Förlag:Springer International Publishing AG
ISBN:9783031706288

Utforska kategorier

Databaser inom Data och IT
Artificiell intelligens inom Data och IT
Tillämpad datateknik inom Data och IT

Innehållsförteckning

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.