Information extraction for retrieval in dynamic system administration

计算机网络，特别是托管服务器场，已经成为非常复杂的系统，24/7服务的要求要求快速纠正任何网络和服务器故障。纠正网络和服务器故障是一个耗时且知识密集型的过程，需要深入了解网络设置和相关监控工具。系统管理员需要能够根据任何可用的信息(故障单、诊断报告、日志文件数据)诊断问题，以便快速识别和执行所需的纠正措施。在实践中，网络和网络托管服务器系统管理员随着时间的推移积累经验，但为他人记录他们的经验是耗时的，因此不切实际。据估计，大部分故障排除时间花在故障排除流程的两个步骤上，(1)确定根本原因，(2)规划解决方案。如果能找到解决方案，将故障排除时间减少30%，对于雇用数千名技术支持人员的典型ISP公司来说，将节省数千万美元的成本。NetPal-2项目的目标是开发第二代故障排除助手：(1)自动提取已经解决的问题案例的相关信息并保存到系统特定的知识库中；(2)从知识库中检索与未解决问题潜在相关的案例，从而帮助系统管理员识别原因并找到解决方案，速度比查找通用Web资源快得多。

Computer networks, and in particular hosting server farms, have become very complex systems, and the requirement for 24/7 service requires fast correction of any network and server failures. Correcting network and server failures is a time-consuming, knowledge-intensive process that requires in-depth knowledge of the network setup and the relevant monitoring tools. System administrators need the ability to diagnose the problem from any information available (trouble tickets, diagnostic reports, log file data), in order to identify and perform quickly the required corrective actions. In practice, network and web hosting server system administrators accumulate experience over time, but documenting their experience for others is time consuming and therefore impractical. It is estimated that most of the troubleshooting time is spent on two steps of the troubleshooting process, (1) Identification of the root cause, and (2) Planning the resolution. The cost savings to a typical ISP company that employs thousands of technical support staff will be in the tens of millions of dollars if a solution can be found to reduce the troubleshooting time by 30%. The objective of the NetPal-2 project is to develop a second-generation troubleshooting assistant that: (1) automatically extracts and saves into a system-specific knowledge base the relevant information to problem cases already resolved; (2) retrieves cases from the knowledge base that are potentially relevant to an unresolved problem, thereby helping the system administrator to identify causes and locate a solution much faster than looking up generic Web resources.