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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
or2yw: Modeling and Visualizing OpenRefineHistories as Ye...
Nikolaus Nova Parulian, Lan Li, Bertram Ludaescher · 2021-12-16 · via cs.DB updates on arXiv.org

OpenRefine is a popular open-source data cleaning tool. It allows users to export a previously executed data cleaning workflow in a JSON format for possible reuse on other datasets. We have developed or2yw, a novel tool that maps a JSON-formatted OpenRefine operation history to a YesWorkflow (YW) model, which then can be visualized and queried using the YW tool. The latter was originally developed to allow researchers a simple way to annotate their program scripts in order to reveal the workflow steps and dataflow dependencies implicit in those scripts. With or2yw the user can automatically generate YW models from OpenRefine operation histories, thus providing a 'workflow view' on a previously executed sequence of data cleaning operations. The or2yw tool can generate different types of YesWorkflow models, e.g., a linear model which mirrors the sequential execution order of operations in OpenRefine, and a \emph{parallel model} which reveals independent workflow branches, based on a simple analysis of dependencies between steps: if two operations are independent of each other (e.g., when the columns they read and write do not overlap) then these can be viewed as parallel steps in the data cleaning workflow. The resulting YW models can be understood as a form of prospective provenance, i.e., knowledge artifacts that can be queried and visualized (i) to help authors document their own data cleaning workflows, thereby increasing transparency, and (ii) to help other users, who might want to reuse such workflows, to understand them better.