IEEE Papers Mapper

Overview

IEEE Papers Mapper is a comprehensive tool for retrieving, processing, classifying, and visualizing research papers from the IEEE Xplore API. It automates data ingestion, applies machine learning for classification, and offers interactive dashboards for insights.

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Table of Contents

• IEEE Papers Mapper
  • Overview
  • Key Features
  • Installation
  • Usage
  • Documentation
  • Testing
  • Contributing
  • Roadmap
  • License
  • Acknowledgments
  • Contact

Demo

Key Features

• Automated Data Retrieval: Scheduled fetching of research papers using APScheduler.
• Data Processing: Cleans, formats, and prepares data for analysis.
• Machine Learning Classification: Zero-shot classification using transformer models.
• Interactive Dashboard: Visualize categorized papers and insights using Plotly Dash.

Installation

Prerequisites

• Python 3.12+
• Virtual Environment (optional but recommended)
• Required tools: pip, git

Steps (for Usage)

1. Create a project directory:

   mkdir ~/workspace/my_project
   cd ~/workspace/my_project
   

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # For Linux/Mac
   venv\Scripts\activate     # For Windows
   

3. Install the pip package and start using it at will:

   pip install ieee-papers-mapper
   

Steps (for Development)

1. Clone the repository

   git clone https://github.com/alex-anast/ieee-papers-mapper.git
   cd ieee-papers-mapper
   

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # For Linux/Mac
   venv\Scripts\activate     # For Windows
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Install the package locally:

   pip install .
   

Usage

Running the Application

Dashboard

To launch the dashboard, run:

python ieee_papers_mapper/app/dash_webapp.py

Visit http://localhost:8050 to view the dashboard.

Data Pipeline

To run the pipeline of retrieving, processing and classifying the papers automatically, execute:

python ieee_papers_mapper/main.py --days 1

NOTE: Currently the scheduler is commented out. The pipeline runs must be executed manually.

Functionality

• Data Retrieval: Automatically fetches new papers based on categories from IEEE Xplore.
• Data Processing: Handles missing columns and formats data for classification.
• Classification: Uses a DeBERTa-v3 model for zero-shot classification into predefined categories.
• Data Storage: Uses SQLite3 for storing the data in an SQL database (scalability, modularity over CSV files).

Documentation

Link to Docs

Complete documentation is available at: https://alex-anast.com/ieee-papers-mapper/

Code structure

./ieee-pappers-mapper
├── conftest.py
├── docs                                # MkDocs
│   ├── about.md
│   ├── developer_guide
│   │   ├── api_reference.md
│   │   └── code_structure.md
│   ├── index.md
│   └── user_guide
│       ├── installation.md
│       ├── overview.md
│       └── usage.md
├── LICENSE
├── mkdocs.yml                          # MkDocs config
├── pyproject.toml
├── README.md
├── requirements.txt
├── setup.py
├── src
│   └── ieee_papers_mapper
│       ├── app                         # Web App (plotly dash)
│       │   ├── assets
│       │   │   └── styles.css
│       │   ├── callbacks.py
│       │   ├── dash_webapp.py
│       │   └── __init__.py
│       ├── config                      # Config and util files
│       │   ├── config.py
│       │   ├── progress.json
│       │   └── scheduler.py            # Custom scheduler wrapper class
│       ├── data
│       │   ├── classify_papers.py      # Classification
│       │   ├── database.py             # Custom Database wrapper class
│       │   ├── get_papers.py           # Paper retrieval
│       │   ├── __init__.py
│       │   ├── pipeline.py             # Pipeline actions
│       │   └── process_papers.py       # Paper (pre)processing
│       ├── ieee_papers.db
│       ├── __init__.py
│       └── main.py
└── tests
    ├── __init__.py
    ├── test_classify_papers.py
    ├── test_database.py
    ├── test_get_papers.py
    └── test_process_papers.py

Testing

Run the tests with:

python -m pytest

Testing Coverage

• get_papers.py: Validates API integration and error handling.
• process_papers.py: Ensures data cleaning and formatting.
• classify_papers.py: Verifies ML classification accuracy and runtime performance.
• database.py: Checks database initialization and CRUD operations.

Contributing

Guidelines

• Fork the repository and submit a pull request.
• Adhere to PEP 8 code style.
• Include unit tests for new core functionality.
• Lint with black formatter.

Roadmap

Future Features

1. Currently author index terms is not consistent, and therefore commented out. Fix.
2. Scheduler is not enabled.
3. Add more advanced ML models for classification.
4. Enhance the dashboard with dynamic filtering.

Known Issues

Limited to 20 API calls/day and to max 200 papers/call, due to IEEE Xplore API restrictions.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Inspiration

This project is a recreated minimal duplicate to my internship at Toyota Motor Europe, Belgium.

Special Thanks

To my mentors at TME.

Contact

• Owner: Alexandros Anastasiou
• Email: anastasioyaa@gmail.com