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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
Breaking the Barriers of Database-Agnostic Transactions
Toshihiro Suzuki, Hiroyuki Yamada · 2026-02-23 · via cs.DB updates on arXiv.org

Federated transaction management has long been used as a method to virtually integrate multiple databases from a transactional perspective, ensuring consistency across the databases. Modern approaches manage transactions on top of a database abstraction to achieve database agnosticism; however, these approaches face several challenges. First, managing transactions on top of a database abstraction makes performance optimization difficult because the abstraction hides away the details of underlying databases, such as database-specific capabilities. Additionally, it requires that application data and the associated transaction metadata be colocated in the same record to allow for efficient updates, necessitating a schema migration to run federated transactions on top of existing databases. This paper introduces a new concept in such database abstraction called Atomicity Unit (AU) to address these challenges. AU enables federated transaction management to aggressively pushdown database operations by making use of the knowledge about the scope within which they can perform operations atomically, fully harnessing the performance of the databases. Moreover, AU enables efficient separation of transaction metadata from application data, allowing federated transactions to run on existing databases without requiring a schema migration or significant performance degradation. In this paper, we describe AU, how AU addresses the challenges, and its implementation within ScalarDB, an open-sourced database-agnostic federated transaction manager. We also present evaluation results demonstrating that ScalarDB with AU achieves significantly better performance and efficient metadata separation.