Discuss: Eaglescope Automatic Configuration

[birm]

Discuss, or Question and answer about this project specifically.

[SomeDieYoung27]

So,I've been diving into the Eaglescope codebase and I'm excited to share my progress and some thoughts.

Here's a quick rundown of what I've done:

I've read through the README.md and the documentation in the docs folder to get an overview

Also, I've got the app running locally to see the code come to life, which helped me visualize the whole thing.

I've navigated the source and components directories, checking out the BarChart.js, DensityChart.js, and similar files to get a sense of the visualization setup there is definately some progress on that behalf.

I tried to trace how data flows into the components and how it's used for rendering the visuals.

I think I am getting a good grasp of how things are structured, especially the use of React contexts for data management and the setup for visualization components. The event handling for data filtering was particularly intriguing.

Now, I have a few questions that I'm hoping to get your input on:

Could you please share your thoughts on the statistical methods we're considering for the automatic configuration feature? And how we'll choose which methods suit different data scenarios?

Are there any specific JS libraries or tools you'd recommend for integrating statistical analysis into the visualization workflows?

Given what I've reviewed, could you point out which parts of the codebase—like DataManager, filterTools, etc or any other part which might be critical for me to look into next?

How do we typically approach the selection of statistical methods for various types of data in the project?

With performance in mind, especially for large datasets, are there certain practices or patterns we follow to ensure the whole process is like umm optimum?

I'm planning to continue exploring the codebase thoroughly today, so any advice or suggestions you have would be great.

Thanks a lot ,I am hoping to get some serious progress in this project in this coming week.

[SomeDieYoung27]

Suggestions

For the data analysis phase, we can categorize each column of the dataset into types such as numerical, categorical, ordinal, or datetime. Simple logic will be employed, for instance, columns with a finite set of text values will be considered categorical, whereas columns with floating-point values may be classified as continuous numerical data.

To facilitate this analysis, we could leverage libraries such as Pandas for python or d3.js for js, which would perform basic statistical measures like for understanding stuff like data distribution and frequency count.

In terms of visualization mapping , we could establish a set of rules for coerraltion. Like bar charts might be used for categorical data, while scatter plots could be employed for examining numerical correlations.

Alternatively, I propose the development of a ml-based recommender system. This system would learn from both user interactions and dataset features for suggesting This includes scripting to analyze uploaded datasets, extracting crucial features such as the number of rows/columns, column types, and statistical summaries for numerical columns. After feature engineering, collaborative filtering models can be used to recommend visualizations based on behaviors of similar users.

Building on the data analysis component and visualization criteria, we could aim to create a module that autonomously suggests visualizations. This module will then be seamlessly integrated with the frontend.

Furthermore, we could plan to establish a user interaction feedback loop. This mechanism will enable the collection of user feedback on the suggested visualizations, thereby enhancing the system's recommendation capabilities through iterative refinements.

what do you think of this approach?

TL;DR:
advancing the project by categorizing dataset columns, utilizing libraries for statistical analysis, and establishing visualization mapping criteria or another anternative approach involves developing a ML recommender system that learns from user interactions and dataset features. The outcome will be a module that automatically suggests visualizations, integrated with the frontend and improved through user feedback.

[MohamedNasser8]

Hi @birm, I am interested in this idea and I have explored the eaglescope code. I think we should use the following methods to implement this idea:

Statistical Analysis:

• Perform exploratory data analysis (EDA) to understand the data's distributions, correlations, and patterns.
• Apply classical statistical methods to find the most significant features or combinations of features for visualization.
• Use techniques such as correlation analysis, chi-square tests, ANOVA, and regression analysis, depending on the type and goal of the data analysis.

Recommender System Development:

• Build a recommender system using AI techniques such as collaborative filtering or content-based filtering.
• For collaborative filtering, use methods like matrix factorization or deep learning-based approaches using TensorFlow.
• For content-based filtering, use similarity measures between features to recommend visualizations that match the data's characteristics.

Next Steps:

• Enable users to rate the recommended visualizations based on their relevance or usefulness.
• Offer options for users to provide comments or suggestions for improvement.
• Incorporate user feedback into the recommendation algorithm to improve its relevance and accuracy.
• Implement a feedback loop to update and refine the recommendation algorithm based on new user interactions and feedback.

Also i think we can benefit from dimentionality reduction techniques like ICA and PCA that can be applied to extract meaningful features or components from the data, which can then be used for visualization, analysis, or recommendation purposes.

[MohamedNasser8]

So can I begin with adding some of thess features and make PR @birm ?