What question did this study set out to answer?

To develop a scalable archiving framework that addresses data management challenges in telecom catalog systems.

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

View Full Paper

Ask AI

Bookmark

Authors

A rule-based and AI-supported framework improves data management in large-scale telecom systems, indicating significant optimization potential.

To develop a scalable archiving framework that addresses data management challenges in telecom catalog systems.
Developed a hybrid framework with a rule-based pre-archiving system defined by domain experts.
Implemented AI-based access analysis to identify frequently accessed data.
Conducted initial evaluations of the archiving potential for different data types.
Achieved up to 51% archiving potential for EPOS offers and 16% for campaign data.
Reduced average query response times from 600–750 ms to 300–400 ms.
Provided a sustainable data management solution without structural changes to the existing system.

Goksu et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

A rule-based and AI-supported framework improves data management in large-scale telecom systems, indicating significant optimization potential.

To develop a scalable archiving framework that addresses data management challenges in telecom catalog systems.
Developed a hybrid framework with a rule-based pre-archiving system defined by domain experts.
Implemented AI-based access analysis to identify frequently accessed data.
Conducted initial evaluations of the archiving potential for different data types.
Achieved up to 51% archiving potential for EPOS offers and 16% for campaign data.
Reduced average query response times from 600–750 ms to 300–400 ms.
Provided a sustainable data management solution without structural changes to the existing system.

Goksu et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark