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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
Integrating Space, Time, Version and Scale Using Alexandr...
Norbert Paul, Patrick Erik Bradley, Martin Breunig · 2013-03-12 · via cs.DB updates on arXiv.org

This article introduces a novel approach to spatial database design. Instead of extending the canonical Solid-Face-Edge-Vertex schema by, say, "hypersolids" these classes are generalised to a common type SpatialEntity, and the individual BoundedBy relations between two consecutive classes are generalised to one BoundedBy relation on SpatialEntity instances. Then the pair (SpatialEntity, BoundedBy) is a so-called incidence graph. The novelty about this approach uses the observation that an incidence graph represents a topological space of SpatialEntity instances because the BoundedBy-relation defines a so-called Alexandrov topology for them turning them into a topological space. So spatial data becomes part of mathematical topology and topology can be immediately applied to spatial data. For example, continuous functions between two instances of spatial data allow the consistent modelling of generalisation. Further, it is also possible to establish a formal topological definition of spatial data dimension, and every topological data model of arbitrary dimension gets a simple uniform data model. This model covers space-time, and the version history of a spatial model can be represented by an Alexandrov topology, too. By integrating space, time, version, and scale into one single schema, topological queries across those aspects are enabled through topological constructions. In fact, the topological constructions cover a relationally complete query language for spaces and can be redefined to operate accordingly on their graph representations. With these observations a relational database schema for a spatial data model of dimension 6 and more is developed. The schema seamlessly integrates 4D space-time, levels of detail and version history, and it can be easily expanded to also contain non-spatial information or be linked to other data sources.