Automating Date Format Detection for Data Visualization
Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. To address this, we present two algorithms, one based on minimum entropy and the other on natural language modeling that automatically derive date formats from string data. These algorithms achieve over 90% accuracy on a large corpus of data columns, streamlining the data preparation process within visualization environments. The minimal entropy approach is particularly fast, providing interactive feedback. Our methods simplify date format extraction, making them suitable for integration into data visualization tools and databases.
Code (0)
등록된 구현이 없습니다.
Tasks
Data VisualizationLanguage ModelingLanguage ModellingSimilar Papers 제목 키워드 기반
Automating Visualization Quality Assessment: a Case Study in Higher Education
We present a case study in the use of machine+human mixed intelligence for visualization quality assessment, applying automated visualization quality metrics to support the human assessment of data visualizations produce…
Is this chart lying to me? Automating the detection of misleading visualizations
Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccurate conclusions. Prior work has shown t…
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping
The Cameras for Allsky Meteor Surveillance (CAMS) project, funded by NASA starting in 2010, aims to map our meteor showers by triangulating meteor trajectories detected in low-light video cameras from multiple locations …
Active LearningData VisualizationMoSculp: Interactive Visualization of Shape and Time
We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space. Given an input video, our…
3D geometryChatVis: Automating Scientific Visualization with a Large Language Model
We develop an iterative assistant we call ChatVis that can synthetically generate Python scripts for data analysis and visualization using a large language model (LLM). The assistant allows a user to specify the operatio…
Language ModelingLanguage ModellingLarge Language Model