Introduction

Welcome to ARGUS – Automated Research Guidance & User Support, a groundbreaking software application designed to revolutionize the literature review process for researchers across diverse academic disciplines. ARGUS emerges as a one-stop solution, offering a suite of functionalities that automate key tasks, enhance information visualization, and empower researchers to dedicate more time to critical analysis and discovery.

Challenges of Traditional Literature Reviews

Traditional literature reviews, while valuable, are often hindered by several limitations:

  • Time-Consuming: Researchers invest significant time in manually searching for relevant articles across multiple databases and screening individual papers, leading to delays in research progress.

  • Error-Prone: Manual extraction of key data and synthesis of information from research articles are prone to human error, impacting the accuracy and reliability of the review.

  • Limited Focus: Repetitive tasks consume valuable time, restricting researchers from conducting in-depth analysis and exploring emerging trends within the literature.

ARGUS: A Solution for Streamlined Reviews

ARGUS addresses these challenges by providing an integrated set of tools that automate document retrieval, facilitate assisted reading, and enable efficient information extraction. By overcoming these limitations, ARGUS empowers researchers to conduct more efficient, accurate, and insightful literature reviews, ultimately accelerating scientific discovery.

System Design: ARGUS - A Modular Approach

ARGUS employs a modular architecture designed for efficiency and scalability, comprising several key components:

  1. User Interface (Front-End): Developed using ReactJS, the user interface offers a seamless experience for uploading research papers, specifying search queries, and displaying ranked results with keyword highlighting.

  2. Back-End (Server-Side): Powered by ExpressJS, the back-end efficiently handles user interactions, processes search queries, and interacts with data storage and processing components.

  3. Data Storage: Initially adopting a folder-based approach for simplicity, ARGUS will integrate database solutions like MySQL or PostgreSQL to manage larger datasets in the future.

  4. Data Processing Libraries: Leveraging libraries like Python2HTMLex and pdf-dist, ARGUS converts PDFs to HTML, extracts text content, and calculates cosine similarity scores for document retrieval.

ARGUS, with its modular architecture and specific technology stack, delivers efficient and scalable functionalities for researchers conducting literature reviews. As ARGUS continues to evolve, upgrades to data storage solutions and integration of additional libraries will enhance its capabilities, further empowering researchers in their pursuit of knowledge and discovery.