This project consists of a web-based application designed to demonstrate a collaborative intelligence component. It's an industrial pilot that showcases the functionalities of data visualization and manipulation using HTML, CSS, and JavaScript.
- Display of data in a table format.
- Data loading from a CSV file.
- Dynamic interaction with data (e.g., filtering and status updating).
- Custom CSS styling for a pleasant user interface.
- Use of Google Fonts for typography.
- Interactive chart for data visualization.
- HTML: The HTML file contains the basic structure of the web page, including headers, a table for displaying data, and a section for charts.
- CSS: The CSS within the
<style>
tag in the HTML head provides custom styling for the web page, including fonts, colors, and layout designs. - JavaScript: The script at the end of the body handles data loading, parsing, and dynamic updating of the table and chart.
- Viewing the Page: Open the HTML file in a web browser to view the interface.
- Loading Data: Click on the 'Load CSV' button to upload and display data from a CSV file.
- Interacting with Data: Use radio buttons to change the score types and interact with the data in the table.
- Printing Non-OK Rows: Click on 'Print Non-OK Rows to File' to save rows with a non-OK status to a file.
- Styling: Modify the CSS in the
<style>
tag to customize the look and feel of the web page. - Data Handling: Adjust the JavaScript functions to change how data is loaded, parsed, and displayed.
- A modern web browser.
- A CSV file for data upload.
For academic use, please refer to our work:
@Inbook{Hoch2024,
author="Hoch, Thomas
and Martinez-Gil, Jorge
and Pichler, Mario
and Silvina, Agastya
and Heinzl, Bernhard
and Moser, Bernhard
and Eleftheriou, Dimitris
and Estrada-Lugo, Hector Diego
and Leva, Maria Chiara",
editor="Soldatos, John",
title="Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0",
bookTitle="Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="407--421",
isbn="978-3-031-46452-2",
doi="10.1007/978-3-031-46452-2_23",
url="https://doi.org/10.1007/978-3-031-46452-2_23"
}
This work is performed in the context of the AI REDGIO 5.0 “Regions and (E)DIHs alliance for AI-at-the-Edge adoption by European Industry 5.0 Manufacturing SMEs” EU Innovation Action Project under Grant Agreement No 101092069