On Automating XSEDE User Ticket Classification

Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.) Pub Date : 2014-07-13 DOI:10.1145/2616498.2616549

Gwang Son, Victor Hazlewood, G. D. Peterson

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引用次数: 11

Abstract

The XSEDE ticket system, which is a help desk ticketing system, receives email and web-based problem reports (i.e., tickets) from users and these tickets can be manually grouped into predefined categories either by the ticket submitter or by operations staff. This manual process can be automated by using text classification algorithms such as Multinomial Naive Bayes (MNB) or Softmax Regression Neural Network (SNN). Ticket subjects, rather than whole tickets, were used to make an input word list along with a manual word group list to enhance accuracy. The text mining algorithms used the input word list to select input words in the tickets. Compared with the Matlab svm() function, MNB and SNN showed overall better accuracy (up to ~85.8% using two simultaneous category selection). Also, the service provider resource (i.e., system name) information could be extracted from the tickets with ~90% accuracy.

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自动化XSEDE用户票证分类

XSEDE票务系统是一个帮助台票务系统，接收来自用户的电子邮件和基于web的问题报告(即票务)，这些票务可以由票务提交者或操作人员手动分组到预定义的类别中。这个手动过程可以通过使用文本分类算法(如多项朴素贝叶斯(MNB)或Softmax回归神经网络(SNN))自动完成。为了提高准确性，我们使用票证主题，而不是整个票证，来制作一个输入词列表和一个手动词组列表。文本挖掘算法使用输入词列表来选择门票中的输入词。与Matlab支持向量机()函数相比，MNB和SNN总体上具有更好的准确率(在两个同时选择类别的情况下，准确率高达85.8%)。此外，服务提供者资源(即系统名称)信息可以以90%的准确率从票证中提取出来。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)

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