Enhancing Intent-Driven Networking with Granular and Aspect Approach

Intent-driven networking propelled by new networking concepts as well as the introduction of achievements from machine learning and artificial intelligence domains in network management, has gained a significant momentum in telco networks automation realm. The fact that automation is closely related to machine learning and artificial intelligence domains, implies the necessity for its interaction with the knowledge management domain. Knowledge management supports intent-driven networking in various ways, ranging from user intent translation, across network feedback analysis to activity deciding mechanisms. This paper presents the granular and aspect-driven approach in knowledge management as a convenient methodology intrinsically compatible with intent-driven core principles.