Self- Healing Problems Systems to Support IT Helpdesk

Abstract

Introduction: focuses on the development and implementation of a self-healing problem system aimed at revolutionizing IT helpdesk operations. The system leverages Case-Based Reasoning (CBR) combined with machine learning to autonomously diagnose and resolve recurring IT issues.

Objectives: The research highlights the inefficiencies of traditional helpdesk frameworks, emphasizing the need for automation to handle complex and high-volume IT queries effectively. By incorporating predictive algorithms, rule-based solutions, and real-time problem-solving mechanisms, the system aims to reduce downtime, improve user satisfaction, and enhance operational efficiency.

Methods: Experimental results demonstrate the efficacy of five similarity functions— Manhattan, Euclidean, Canberra, Squared Chord, and Squared Chi-Squared through the case base reasoing.

Results: in identifying and resolving IT issues across three categories: managers, employees, and students. The Manhattan function consistently achieved the highest accuracy, with 89.9% for manager cases, 65.6% for employees, and 54.6% for students. Error rates calculated using the Root Mean Square Error (RMSE) revealed similar trends, with the Manhattan function demonstrating strong reliability across all categories. For instance, the error rates for Manhattan were 27.76 for managers, 21.01 for employees, and 16.34 for students. Conversely, other functions like Canberra and Squared Chord exhibited limited effectiveness, particularly for complex or diverse cases

Conclusions: These results affirm the system’s ability to adapt to varying data complexities, making it a robust solution for modern IT challenges. Future research should focus on enhancing these systems’ scalability and exploring advanced analytics for broader applications in dynamic IT environments.