A Scalable Rule-Based and Ai-Assisted Framework for Archiving in Large-Scale Telecom Catalog Systems

Abstract

This study presents a rule-based and AI-supported archiving strategy developed to address the data management and performance challenges observed in Turkcell’s Central Product Catalog Management (CPCM) system. As the volume of data has steadily increased, the system has begun to face critical issues related to performance and long-term sustainability. Traditional archiving techniques were deemed inadequate due to the system’s versioning architecture and strict regulatory constraints. To overcome these limitations, we propose a hybrid framework consisting of two stages: (1) rule-based pre-archiving defined by domain expertise, (2) AI-based access analysis to identify frequently accessed data to ensure scalability and operational continuity. Initial evaluations indicate significant archiving potential: up to 51% for EPOS offers and 16% for campaign data. Furthermore, the framework aims to reduce average query response times from 600–750 ms to 300–400 ms without requiring structural changes to the monolithic system. This approach offers a practical solution for sustainable data management in large-scale telecommunications systems and contributes to academic discussions on hybrid archiving strategies, performance optimization, and regulatory compliance in enterprise information systems.

Keywords

• Hybrid data archiving
• Rule-based & AI-assisted decision-making
• Telecom product catalog management (CPCM)
• Regulatory compliance (BTK/GDPR)
• Performance optimization in large-scale monolithic systems.

Büyük Ölçekli Telekom Katalog Sistemlerinde Arşivleme için Ölçeklenebilir, Kural Tabanlı ve Yapay Zekâ Destekli Bir Çerçeve

Abstract

Bu çalışma, Turkcell’in Merkezi Ürün Katalog Yönetimi (CPCM) sisteminde gözlemlenen veri yönetimi ve performans sorunlarını ele almak üzere geliştirilen kural tabanlı ve yapay zekâ destekli bir arşivleme stratejisi sunmaktadır. Veri hacminin istikrarlı biçimde artmasıyla birlikte, sistem performans ve uzun vadeli sürdürülebilirliğe ilişkin kritik güçlüklerle karşılaşmaya başlamıştır. Sistemin sürümleme odaklı mimarisi ve sıkı düzenleyici kısıtlar nedeniyle, geleneksel arşivleme teknikleri yetersiz bulunmuştur. Bu sınırlamaların üstesinden gelmek amacıyla iki aşamalı hibrit bir çerçeve önermekteyiz: (1) alan uzmanlığıyla tanımlanan kural tabanlı ön arşivleme ve (2) ölçeklenebilirliği ile operasyonel sürekliliği güvence altına almak için sık erişilen verileri belirlemeye yönelik yapay zekâ temelli erişim analizi. İlk değerlendirmeler, EPOS teklifleri için %51’e ve kampanya verileri için %16’ya varan kayda değer bir arşivleme potansiyeline işaret etmektedir. Ayrıca çerçeve, monolitik sistemde yapısal değişiklik gerektirmeksizin ortalama sorgu yanıt sürelerini 600–750 ms aralığından 300–400 ms aralığına düşürmeyi hedeflemektedir. Bu yaklaşım, büyük ölçekli telekomünikasyon sistemlerinde sürdürülebilir veri yönetimi için pratik bir çözüm sunmakta ve kurumsal bilgi sistemlerinde hibrit arşivleme stratejileri, performans optimizasyonu ve düzenleyici uyum konularındaki akademik tartışmalara katkı sağlamaktadır.

Keywords

• Hibrit veri arşivleme
• Kural tabanlı ve YZ destekli karar verme
• Telekom ürün katalog yönetimi (CPCM)
• Düzenleyici uyum (BTK/GDPR)
• Büyük ölçekli monolitik sistemlerde performans optimizasyonu

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