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

D
DataBreaches.Net
N
Netflix TechBlog - Medium
F
Fortinet All Blogs
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
宝玉的分享
宝玉的分享
Y
Y Combinator Blog
博客园 - 聂微东
WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
B
Blog RSS Feed
小众软件
小众软件
The GitHub Blog
The GitHub Blog
S
SegmentFault 最新的问题
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
Microsoft Azure Blog
Microsoft Azure Blog
V
V2EX
B
Blog
H
Help Net Security
D
Docker
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
罗磊的独立博客
月光博客
月光博客
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
Mixing set and bag semantics
Wilmer Ricciotti, James Cheney · 2019-05-06 · via cs.DB updates on arXiv.org

The conservativity theorem for nested relational calculus implies that query expressions can freely use nesting and unnesting, yet as long as the query result type is a flat relation, these capabilities do not lead to an increase in expressiveness over flat relational queries. Moreover, Wong showed how such queries can be translated to SQL via a constructive rewriting algorithm. While this result holds for queries over either set or multiset semantics, to the best of our knowledge, the questions of conservativity and normalization have not been studied for queries that mix set and bag collections, or provide duplicate-elimination operations such as SQL's $\mathtt{SELECT}~\mathtt{DISTINCT}$. In this paper we formalize the problem, and present partial progress: specifically, we introduce a calculus with both set and multiset collection types, along with natural mappings from sets to bags and vice versa, present a set of valid rewrite rules for normalizing such queries, and give an inductive characterization of a set of queries whose normal forms can be translated to SQL. We also consider examples that do not appear straightforward to translate to SQL, illustrating that the relative expressiveness of flat and nested queries with mixed set and multiset semantics remains an open question.