Automating Date Format Detection for Data Visualization

--Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. T o 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. Lately, the coordination of information perception advancements like Polaris [1] and Spotfire [2] has featured the significance of joining computational power with human knowledge for successful information examination. While PCs succeed at handling huge datasets, people bring significant space skill and the capacity to perceive designs visually [3], [4]. Frameworks that influence both human criticism and machine handling demonstrate additional success in separating significant experiences from information. Intuitive perception frameworks have become fundamental for empowering clients to investigate information while keeping up with their scientific stream.