Kbn Network

Abstract

1. Introduction

This is a plugin developed for Kibana that displays a network node that link two fields that have been previously selected.

2. Objective

Network Plugin for Kibana

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

4. Technology Stack

5. System Requirements

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

• Node.js (LTS) and npm
• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/dlumbrer/kbn_network.git
cd kbn_network

1. Go to releases and download the right one for your Kibana
2. unzip/untar it into KIBANA_HOME/plugins
3. Start your Kibana
4. Move into plugins folder: cd KIBANA_HOME/plugins
5. Clone the source code (it depends on your Kibana's version):
6. Kibana 7.5 and upper: git clone https://github.com/dlumbrer/kbn_network.git network_vis -b 7-dev
7. Kibana 7.0 to 7.4: git clone https://github.com/dlumbrer/kbn_network.git network_vis -b 7.4-dev
8. Kibana 6.6 and upper: git clone https://github.com/dlumbrer/kbn_network.git network_vis -b 6-dev

cd network_vis
      rm -rf images/
      npm install

cd KIBANA_HOME
rm -rf plugins/network_vis/

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 dlumbrer 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.