惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

有赞技术团队
有赞技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
I
InfoQ
J
Java Code Geeks
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
D
DataBreaches.Net
Recent Announcements
Recent Announcements
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog RSS Feed
Y
Y Combinator Blog
博客园 - 【当耐特】
博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
大猫的无限游戏
大猫的无限游戏
P
Proofpoint News Feed
量子位
C
Check Point Blog
F
Fortinet All Blogs
罗磊的独立博客
Last Week in AI
Last Week in AI
GbyAI
GbyAI
L
LangChain Blog
博客园 - 司徒正美

cs.DB updates on arXiv.org

Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
Elimination of annotation dependencies in validation for ...
Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghe · 2025-03-14 · via cs.DB updates on arXiv.org

JSON Schema is a logical language used to define the structure of JSON values. JSON Schema syntax is based on nested schema objects. In all versions of JSON Schema until Draft-07, collectively known as Classical JSON Schema, the semantics of a schema was entirely described by the set of JSON values that it validates. This semantics was the basis for a thorough theoretical study and for the development of tools to decide satisfiability and equivalence of schemas. Unfortunately, Classical JSON Schema suffered a severe limitation in its ability to express extensions of object schemas, which caused the introduction, with Draft 2019-09, of two disruptive features: annotation dependency and dynamic references. These new features undermine the previously developed semantic theory, and the algorithms used to decide satisfiability for Classical JSON Schema are not easy to extend. One possible solution is rewriting a schema written in Modern JSON Schema into an equivalent schema in Classical JSON Schema. In this paper we prove that the elimination of annotation dependent keywords cannot, in general, avoid an exponential increase of the schema dimension. We provide an algorithm to eliminate these keywords that, despite the theoretical lower bound, behaves quite well in practice, as we verify with an extensive set of experiments.