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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
Deep Separability of Ontological Constraints
Andrea Calì, Marco Console, Riccardo Frosini · 2013-12-20 · via cs.DB updates on arXiv.org

When data schemata are enriched with expressive constraints that aim at representing the domain of interest, in order to answer queries one needs to consider the logical theory consisting of both the data and the constraints. Query answering in such a context is called ontological query answering. Commonly adopted database constraints in this field are tuple-generating dependencies (TGDs) and equality-generating dependencies (EGDs). It is well known that their interaction leads to intractability or undecidability of query answering even in the case of simple subclasses. Several conditions have been found to guarantee separability, that is lack of interaction, between TGDs and EGDs. Separability makes EGDs (mostly) irrelevant for query answering and therefore often guarantees tractability, as long as the theory is satisfiable. In this paper we review the two notions of separability found in the literature, as well as several syntactic conditions that are sufficient to prove them. We then shed light on the issue of satisfiability checking, showing that under a sufficient condition called deep separability it can be done by considering the TGDs only. We show that, fortunately, in the case of TGDs and EGDs, separability implies deep separability. This result generalizes several analogous ones, proved ad hoc for particular classes of constraints. Applications include the class of sticky TGDs and EGDs, for which we provide a syntactic separability condition which extends the analogous one for linear TGDs; preliminary experiments show the feasibility of query answering in this case.