Adaptivity in Role-Based Access Control During Stochastic Situations: A Comprehensive Study between Graph and Relational Databases

Abstract

Role-based control is straightforward to implement in static systems where there are specific policies for role and resource mapping. The main challenge is faced in dynamic and unpredictable systems with erratic workflows. Conventional methods prove to be inadequate in dynamic environments. Over the Years methods suggested include probabilistic, machine learning, ontology, and decision tree models to improve the adaptability. However, they fail to build a bridge between operational methods and flexible approaches. Role-based control systems are based on previously defined rules and policies. A truly flexible system should run without human interference, autonomously accessing the user's request and granting the requirements based on it is genuineness. This research introduces the need for a based control methodology, using a project management case study using Neo4j. It generates the responses promptly based on the authenticity of the user We have computed the time required to process the results in in SQL as well as Neo4j. A role-based control system is built to improve coordination among the departments present in the real-life corporate world and transfer data based on authenticated data requests. Graph databases outperform relational databases by nearly an average of 8 milliseconds on an average across the queries run. This framework demonstrates better flexibility, system adaptability, and precise computational efficiency across various scenarios.