GDL

Journal from gdlhub / 2022-02-14 15:15:56
Oleh : Feras Al-Hawari, Hala Barham, King Saud University
Dibuat : 2022-02-14, dengan 0 file

Keyword : Machine learning, Text classification, Help desk system, Software engineering, IT service management (ITSM), Business process
Url : http://www.sciencedirect.com/science/article/pii/S1319157819300515
Sumber pengambilan dokumen : web

A help desk system that acts as a single point of contact between users and IT staff is introduced in this paper. It utilizes an accurate ticket classification machine learning model to associate a help desk ticket with its correct service from the start and hence minimize ticket resolution time, save human resources, and enhance user satisfaction. The model is generated according to an empirically developed methodology that is comprised of the following steps: training tickets generation, ticket data preprocessing, words stemming, feature vectorization, and machine learning algorithm tuning. Nevertheless, the experimental results showed that including the ticket comments and description in the training data was one of the main factors that enhanced the model prediction accuracy from 53.8% to 81.4%. Furthermore, the system supports an administrator view that facilitates defining offered services, administering user roles, managing tickets and generating management reports. Also, it offers a user view that allows employees to report issues, request services, and exchange information with the IT staff via help desk tickets. Moreover, it supports automatic email notifications amongst collaborators for further action. Yet, it helps in defining business processes with well-defined activities and measuring KPIs to assess the performance of IT staff and processes.

Deskripsi Alternatif :

A help desk system that acts as a single point of contact between users and IT staff is introduced in this paper. It utilizes an accurate ticket classification machine learning model to associate a help desk ticket with its correct service from the start and hence minimize ticket resolution time, save human resources, and enhance user satisfaction. The model is generated according to an empirically developed methodology that is comprised of the following steps: training tickets generation, ticket data preprocessing, words stemming, feature vectorization, and machine learning algorithm tuning. Nevertheless, the experimental results showed that including the ticket comments and description in the training data was one of the main factors that enhanced the model prediction accuracy from 53.8% to 81.4%. Furthermore, the system supports an administrator view that facilitates defining offered services, administering user roles, managing tickets and generating management reports. Also, it offers a user view that allows employees to report issues, request services, and exchange information with the IT staff via help desk tickets. Moreover, it supports automatic email notifications amongst collaborators for further action. Yet, it helps in defining business processes with well-defined activities and measuring KPIs to assess the performance of IT staff and processes.

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