dblp-analysis-tool

dblp-analysis-tool is a web application developed for analyzing the dblp computer science bibliography. This project was undertaken as part of my Bachelor's thesis at Palacký University Olomouc, supervised by RNDr. Martin Trnečka, Ph.D. This repository contains both the source code of the tool and the text of the Bachelor's thesis.

dblp computer science bibliography

dblp computer science bibliography, a leading publicly accessible database and web service, provides metadata of publications from various fields of computer science. This includes a vast collection from major journals and conference proceedings, as well as scientific monographs and encyclopedias. As of 2025, the database contains more than 7 million publications by over 3 million authors worldwide.

Features

dblp provides valuable data for many researchers and institutions. However, effectively understanding, comparing, tracking trends, and conducting analysis requires a clear and concise presentation of this data and its underlying relationships.

The dblp-analysis-tool addresses this need by providing a user-friendly interface for visualizing and analyzing dblp data. This tool allows users to:

• visualize author, journal, and conference statistics using various charts,
• browse and filter publication metadata,
• group authors, analyze their collective statistics, and perform basic analysis,
• explore co-authorship relationships through interactive, filterable graph,
• export analyzed data for further research and analysis.

Showcase

These images highlight some of the key features of the application:

The application's home page

The search dialog

An author's page

Some of the charts available in the application

The co-authorship graph

The filter dialog

Used technologies

This web application was developed primarily using the following technologies:

• Next.js
• Tailwind CSS
• D3.js
• Cheerio
• MongoDB

Running with Docker

To set up the application using Docker, you need to have the latest version of Docker installed along with Docker Compose. In the src/ directory, there are two Docker Compose files:

• docker-compose-dev.yml – for local testing of the application in production mode (the application runs on port 3000).
• docker-compose-prod.yml – for deploying the application to production (the application runs on port 80).

Both files ensure the creation and launch of MongoDB and the application itself. The necessary environment variables and application ports are already predefined in these files.

The entire process of setting up the application is the same for both files:

1. Make sure you have Docker running on your machine.

2. In the terminal, navigate to the src/ directory containing the mentioned files.

3. Build Docker containers using the desired file:

   docker compose -f ./docker-compose-dev.yml build
   

   Alternatively:

   docker compose -f ./docker-compose-dev.yml build --no-cache
   

4. Start the Docker containers for the desired file:

   docker compose -f ./docker-compose-dev.yml up
   

Running without Docker

For development purposes, it's more suitable to run the application without Docker. In this case, you need to have a running MongoDB. I chose the community version, which can be installed locally. Additionally, you'll need Node.js installed along with the npm package manager.

The entire process of setting up the application:

1. Make sure you have Node.js and npm installed, and MongoDB running.

2. In the src/ directory, create a .env file for environment variables. Add the MONGODB_URI variable with the database address, for example, for a locally running MongoDB:

   MONGODB_URI=mongodb://127.0.0.1:27017/dblp-tool
   

   You don’t need to create the database with the specified name (for example dblp-tool) beforehand on the database server.

3. In the terminal, navigate to the src/ directory.

4. Install the necessary dependencies using the npm install command.

5. Start the application either in development or production mode:

   1. For development mode, run the application with:

      npm run dev
      

   2. To run the application in production mode, first build it:

      npm run build
      

      Then start the application with:

      npm run start
      

6. The address where the application is running will be displayed in the terminal.