Parallel and Distributed Architecture for Multilingual Open Source Intelligence Systems

Abstract

The proliferation of publicly available information across multiple languages presents both unique challenges and opportunities for Open Source Intelligence (OSINT) systems. This paper proposes a novel architecture for multilingual OSINT that is both parallel and distributed. The architecture integrates language identification and translation capabilities, enabling it to handle linguistically diverse data by transforming it into a unified format for efficient analysis. Designed specifically to address the challenges of parallel and distributed processing in OSINT systems, this architecture aims to offer scalability and performance benefits when dealing with massive data volumes. Our primary focus has been on devising strategies and tactics that address these concerns, providing a robust solution for the collection, processing and analysis of data in various languages. This work marks a significant step towards the development of more globally inclusive OSINT systems.