Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Silva, S., Pereira, R. & Ribeiro, R. (2018). Machine learning in incident categorization automation. In 13th Iberian Conference on Information Systems and Technologies (CISTI). Cáceres: IEEE.
Exportar Referência (IEEE)
S. Silva et al.,  "Machine learning in incident categorization automation", in 13th Iberian Conf. on Information Systems and Technologies (CISTI), Cáceres, IEEE, 2018
Exportar BibTeX
@inproceedings{silva2018_1713994202152,
	author = "Silva, S. and Pereira, R. and Ribeiro, R.",
	title = "Machine learning in incident categorization automation",
	booktitle = "13th Iberian Conference on Information Systems and Technologies (CISTI)",
	year = "2018",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.23919/CISTI.2018.8399244",
	publisher = "IEEE",
	address = "Cáceres",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8399244/"
}
Exportar RIS
TY  - CPAPER
TI  - Machine learning in incident categorization automation
T2  - 13th Iberian Conference on Information Systems and Technologies (CISTI)
AU  - Silva, S.
AU  - Pereira, R.
AU  - Ribeiro, R.
PY  - 2018
DO  - 10.23919/CISTI.2018.8399244
CY  - Cáceres
UR  - https://ieeexplore.ieee.org/document/8399244/
AB  - IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets.
ER  -