Automatic problem extraction and analysis from unstructured text in IT tickets
Automatic problem extraction and analysis from unstructured text in IT tickets
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DOI：
10.1147/jrd.2016.2629318
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发表时间：
2017-01-01
影响因子：
1.3
通讯作者：
Sridhara, G.
中科院分区：
文献类型：
--
作者：
Agarwal, S.;Aggarwal, V.;Sridhara, G.
IT服务是极度人力劳动密集型的，其重点是以低成本提供高效的服务。因此，使用软件服务代理来实现可重复IT任务的自动化，从而减少人工劳动，是服务管理的一个重要组成部分。IT服务人员所做的大部分工作涉及问题的故障排除。然而，IT系统的复杂性使得问题的自动确定和解决成为一个具有挑战性的研究问题。使用先前客户问题和解决方案的数据库，我们构建了一个系统，该系统可以提取有关IT基础设施中出现的不同类型问题的知识，挖掘问题与最近系统更改的联系，并确定解决问题的活动。该系统的核心是使用数据挖掘、机器学习和自然语言解析技术。通过使用提取的知识，人们可以(i)了解影响IT基础设施的问题类型和根本原因，（ii）主动纠正原因，使其不再导致问题，以及（iii）估计服务管理自动化的范围。在未来，对于任何IT公司来说，一个巨大的成本差异通常会涉及到能够从这些技术构建自动化服务代理，这将导致人力资源的减少。
IT services are extremely human labor intensive, and a key focus is to provide efficient services at low cost. Automation of repeatable IT tasks using software service agents that reduce human effort is therefore an important component of service management. A large fraction of the work done by IT service personnel involves troubleshooting of problems. However, the complexity of IT systems makes automated problem determination and resolution a challenging research problem. Using a database of prior customer problems and solutions, we build a system that extracts knowledge about different classes of problems arising in the IT infrastructure, mine problem linkages to recent system changes, and identify the resolution activities to mitigate problems. The system, at its core, uses data mining, machine learning, and natural language parsing techniques. By using extracted knowledge, one can (i) understand the kind of problems and the root causes affecting the IT infrastructure, (ii) proactively remediate the causes so that they no longer result in problems, and (iii) estimate the scope for automation for service management. In the future, a large cost differentiator for any IT company will often involve being able to build automated service agents from these technologies, which will result in a reduction in human effort.