Data Analysis Dashboard: Difference between revisions
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The Data Analysis Dashboard facilitates the comprehensive monitoring of tabular data (e.g., from sensor arrays) with sophisticated processing and analysis capabilities. | The Data Analysis Dashboard facilitates the comprehensive monitoring of tabular data (e.g., from sensor arrays) with sophisticated processing and analysis capabilities. Its key benefits include intuitive visualisation, flexible querying, and the ability to infer patterns and detect anomalies, giving human operators critical decision-making support. It uses some classical Data and Knowledge Engineering methods (e.g., Knowledge Graphs) and is implemented based on the [https://streamlit.io/ Streamlit] framework. | ||
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== Resources == | == Resources == | ||
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<li> | <li>A live demo of this component can be found [https://airedgio-dashboard.streamlit.app/ here]</li> | ||
<li>The source code is available at the following [https://github.com/AI-REDGIO-5-0/data-dashboard/ GitHub repository]</li> | |||
<li>Created by [https://www.scch.at/ Software Competence Center Hagenberg - SCCH]</li> | <li>Created by [https://www.scch.at/ Software Competence Center Hagenberg - SCCH]</li> | ||
<li>Contact ''jorge.martinez-gil@scch.at''</li> | <li>Contact ''jorge.martinez-gil@scch.at''</li> | ||
Revision as of 14:58, 7 October 2025
Dashboard that allows human operators monitor and extract knowledge from tabular data through visualization/interpretability, querying, and inference features.
Asset Description
The Data Analysis Dashboard facilitates the comprehensive monitoring of tabular data (e.g., from sensor arrays) with sophisticated processing and analysis capabilities. Its key benefits include intuitive visualisation, flexible querying, and the ability to infer patterns and detect anomalies, giving human operators critical decision-making support. It uses some classical Data and Knowledge Engineering methods (e.g., Knowledge Graphs) and is implemented based on the Streamlit framework.
Usage
This software helps users analyse datasets and uncover hidden relationships. Initially the user should perform the following:
- Upload a CSV file containing the data to be analysed
- Configure the data preprocessing aspects (handling missing values and data types)
Afterwards the user can explore their data and underlying relations , extracting knowledge in a flexible way:
- Select pairs of features and see the visualisations of correlation analysis
- Build a knowledge graph that represents significant relationships in the data
- Interact directly with the knowledge graph through a SPARQL query interface
Note: The asset is under Ongoing Development
Licence
Restricted
Resources
- A live demo of this component can be found here
- The source code is available at the following GitHub repository
- Created by Software Competence Center Hagenberg - SCCH
- Contact jorge.martinez-gil@scch.at
Acknowledgement
This tool has been mainly developed in the frame of the project TrineFlex from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101058174.