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Browsing by Author "Yurtalan, Gokhan"

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    Parallel and Distributed Architecture for Multilingual Open Source Intelligence Systems
    (Springer international Publishing Ag, 2024) Karamanlioglu, Alper; Yurtalan, Gokhan; Karatas, Yahya Bahadir; 01. Çankaya Üniversitesi
    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.
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    Citation - WoS: 1
    Citation - Scopus: 1
    Redefining Osint Software Architecture With System-Centric Architecture Design: a Framework Shaped by Qaw, Add, and Atam
    (Ieee-inst Electrical Electronics Engineers inc, 2025) Yurtalan, Gokhan; Arslan, Serdar; 06.01. Bilgisayar Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    This study develops a novel software architecture for Open Source Intelligence (OSINT). The primary architectural drivers of the OSINT architecture are identified using the Quality Attribute Workshop (QAW), and an end-to-end OSINT software architecture design is implemented in accordance with Attribute-Driven Design (ADD). The architecture is extensively analyzed with metric evaluations and the Architecture Tradeoff Analysis Method (ATAM), confirming critical quality attributes such as performance, reliability, functional suitability, and security. The design decisions taken within this architectural framework are detailed in the article through module view, component and connector view, and allocation view representations. The proposed architecture uses an on-premise Large Language Model (LLM) to explore the potential for deeper and more reliable information processing capabilities in OSINT analyses and presents a framework that enhances semantic depth and analytical capabilities. The architecture not only amplifies the semantic and analytical capabilities of OSINT systems but also sets a precedent for future architectural endeavors in intelligence systems design. This paper presents a framework that not only meets contemporary needs but also anticipates future demands in the rapidly evolving field of OSINT.