Smoosense

Abstract

1. Introduction

SmooSense is a web-based application for exploring and analyzing large-scale multi-modal tabular data. It provides an intuitive interface for working with CSV, Parquet, and other data formats with powerful SQL querying capabilities.

2. Objective

Interactively browse multimodal tabular data

This project demonstrates how TypeScript can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Natively visualize multimodal data (images, videos, json, bbox, image mask, 3d assets etc)
• Effortlessly look at distribution. Automatic drill-through from statistics to random samples.
• Graphical and interactive slice-n-dice of your dataset.
• Large scale support for 100 million rows on your laptop.
• Easy to integrate; SmooSense directly work with table file (parquet, csv, jsonl, etc)
• Low cost. Free and open source to use on your laptop. Compute efficient when deployed.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/SmooSenseAI/smoosense.git
cd smoosense

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by SmooSenseAI and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.