Abstract
Public and service-oriented organizations increasingly rely on digital ticketing systems to man- age large volumes of citizen and customer requests. Despite this, many ticket management processes remain partially manual, leading to delays in case routing, inefficient resource al- location, and inconsistent resolution times. These challenges can negatively impact service quality, operational efficiency, and user satisfaction, particularly in large-scale public sector environments. This study proposes SmartDesk, a data-driven framework for automatic ticket classification and resolution time prediction using data analytics and natural language process- ing (NLP) techniques. The research leverages publicly available helpdesk and service ticket datasets to simulate real-world service environments where incoming requests vary in complex- ity, urgency, and subject matter. Textual ticket descriptions are preprocessed using standard NLP techniques, including tokenization, stop-word removal, and lemmatization, followed by feature extraction using term frequency–inverse document frequency (TF-IDF). Multiple ma- chine learning classification models are developed and evaluated to automatically assign tickets to the appropriate service category or department. In parallel, regression-based models are employed to predict expected ticket resolution time based on textual features and historical res- olution patterns. Model performance is assessed using accuracy, precision, recall, F1-score, and error metrics to ensure robustness and practical applicability. The findings demonstrate that automated ticket classification can significantly reduce misrouting and manual intervention, while resolution time prediction provides actionable insights for workload planning and service prioritization. The study highlights the potential of analytics-driven ticket management systems to enhance operational efficiency and support timely decision-making in service-oriented and public sector contexts. By adopting data-driven approaches such as SmartDesk, organizations can improve service responsiveness, transparency, and overall user experience.
Publication Date
5-26-2026
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Ioannis Karamitsos
Recommended Citation
Almheiri, Dananir, "SmartDesk: Automatic Ticket Classification and Resolution Prediction Using Data Analytics" (2026). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12709
Campus
RIT Dubai
