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Journals / Journal of Naval Sciences and Engineering / 2026 / Volume: 22 Issue: 1

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

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

Esra Sipahi Göksu0009-0007-9026-2096
Serdar Gönülal0009-0009-8564-3198
Serhan Bulca0009-0008-3690-5799
Batuhan Zümbül0009-0001-3039-2020
Öykü Muslu0009-0007-8646-3856
Gökhan İçen0009-0002-8817-7793
Ayla Gürkal0009-0008-6566-2335
Mucella Duman0009-0001-8509-8195
Esra Akbalik0009-0004-0398-5076
Can Yel0009-0001-3594-4744
Nagihan Koca0009-0008-1929-4789
Serkan Çelenk0009-0006-3515-1265
Alparslan Koçyiğit0009-0000-1184-5701
Barış Er0009-0001-5811-3513
Nehra Karabulut0009-0005-8677-1707
Asaf Karataş0009-0006-4462-8889
Önder Demir0000-0003-4540-663X
Buket Doğan(MARMARA ÜNİVERSİTESİ)0000-0003-1062-2439
Kazım Yıldız(MARMARA UNIVERSITY)0000-0001-6999-1410
Pages
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Publication Date
2026-06-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:As the volume of data has steadily increased, the system has begun to face critical issues related to performance and long-term sustainability.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hybrid data archiving · Rule-based & AI-assisted decision-making · Telecom product catalog management (CPCM)

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.

Özet

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.

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Cite This Article

Göksu, E. S., Gönülal, S., Bulca, S., Zümbül, B., Muslu, Ö., İçen, G., Gürkal, A., Duman, M., Akbalik, E., Yel, C., Koca, N., Çelenk, S., Koçyiğit, A., Er, B., Karabulut, N., Karataş, A., Demir, Ö., Doğan, B., Yıldız, K. (2026). A Scalable Rule-Based and Ai-Assisted Framework for Archiving in Large-Scale Telecom Catalog Systems. Journal of Naval Sciences and Engineering, Volume: 22 Issue: 1. https://doi.org/10.56850/jnse.1840702

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