Veri Füzyonu Sürecine Yönelik Kuramsal ve Metodolojik Bir İnceleme: Mimari Yapılar, Algoritmalar ve Zorluklar

Abstract

Bu çalışma, veri füzyonu sürecini kuramsal ve metodolojik bir perspektiften inceleyerek, farklı uygulama seviyeleri, algoritmalar, mimari modeller ve karşılaşılan zorlukları sistematik biçimde ele almaktadır. Veri füzyonu; sağlık, tarım, çevresel izleme, otonom sistemler, robotik ve endüstriyel alanlar gibi pek çok disiplin için daha güvenilir bilgi üretimi, belirsizliklerin azaltılması ve karar destek mekanizmalarının güçlendirilmesinde kritik bir rol üstlenmektedir. Literatürde, veri füzyon yöntemleri kural tabanlı, olasılık temelli, yapay zekâ tabanlı ve optimizasyon odaklı yaklaşımlar altında sınıflandırılmakta; bu yöntemler farklı senaryo ve veri tiplerine göre avantajlar ve sınırlılıklar sunmaktadır. Ayrıca, merkezî, dağıtık ve hibrit mimariler aracılığıyla tasarlanan sistemler, ölçeklenebilirlik, hata toleransı ve gerçek zamanlılık gibi özellikler açısından değerlendirilmiştir. Çalışma, aynı zamanda veri füzyonu süreçlerinde karşılaşılan temel zorlukları (veri heterojenliği, hizalama problemleri, donanımsal sınırlamalar, gizlilik ve güvenlik kaygıları) tartışarak, bu engellerin aşılmasına yönelik çözüm yaklaşımlarını da ortaya koymaktadır. Sonuç olarak, veri füzyonu yalnızca teknik bir entegrasyon değil, aynı zamanda sürdürülebilir ve güvenilir karar destek sistemleri için stratejik bir metodolojik çerçeve sunmaktadır.

A Theoretical and Methodological Review of the Data Fusion Process: Architectures, Algorithms, and Challenges

Abstract

This study provides a comprehensive theoretical and methodological analysis of the data fusion process, systematically addressing its application levels, algorithms, architectural models, and associated challenges. Data fusion plays a pivotal role across various disciplines—including healthcare, agriculture, environmental monitoring, autonomous systems, robotics, and industrial domains—by generating more reliable information, reducing uncertainty, and strengthening decision-support mechanisms. In the literature, data fusion approaches are classified into rule-based, probabilistic, artificial intelligence-driven, and optimization-oriented methods, each offering distinct advantages and limitations depending on the scenario and data type. Furthermore, centralized, distributed, and hybrid architectures are evaluated in terms of scalability, fault tolerance, and real-time performance. This study also highlights critical challenges in data fusion, such as data heterogeneity, alignment issues, hardware constraints, and privacy and security concerns, while exploring strategies to address these barriers. Ultimately, data fusion is positioned not merely as a technical integration process but as a strategic methodological framework for building sustainable and trustworthy decision-support systems.

Keywords

• Artificial Intelligence
• Data Fusion
• Sensor Integration

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