通过使用多模式分析增强事件管理流程来改进IT支持
Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis
摘要：
IT支持服务行业正在经历一场重大变革，人工智能变得越来越普遍。在IT支持流程中的每个人工接触点，都有大量的自动化工作。事件管理就是这样一个过程，它一直是基于AI的自动化的信标过程。我们的愿景是从事件/票据到达的时间到解决和关闭的过程自动化。虽然文本是沟通事件的主要方式，但使用图像等替代方式来沟通问题的趋势越来越明显。如今，很大一部分IT支持工单都包含以屏幕截图、日志消息、发票等形式附加的图像数据。这些附件有助于更好地解释问题，从而有助于更快地解决问题。任何渴望提供基于AI的IT支持的人，都必须构建能够处理多模式内容的系统。在本文中，我们提出了如何在IT支持领域的事件管理可以更有效地使用多模态分析。从不同模态中提取的信息被关联以丰富票据中的信息，并用于更好的票据路由和解析。我们评估我们的系统使用约25000个真正的门票包含附件从选定的问题领域。我们的研究结果表明，在路由和分辨率的显着改善与使用多模态机票分析相比，只有基于文本的分析。
英文摘要：
IT support services industry is going through a major transformation with AI becoming commonplace. There has been a lot of effort in the direction of automation at every human touchpoint in the IT support processes. Incident management is one such process which has been a beacon process for AI based automation. The vision is to automate the process from the time an incident/ticket arrives till it is resolved and closed. While text is the primary mode of communicating the incidents, there has been a growing trend of using alternate modalities like image to communicate the problem. A large fraction of IT support tickets today contain attached image data in the form of screenshots, log messages, invoices and so on. These attachments help in better explanation of the problem which aids in faster resolution. Anybody who aspires to provide AI based IT support, it is essential to build systems which can handle multi-modal content. In this paper we present how incident management in IT support domain can be made much more effective using multi-modal analysis. The information extracted from different modalities are correlated to enrich the information in the ticket and used for better ticket routing and resolution. We evaluate our system using about 25000 real tickets containing attachments from selected problem areas. Our results demonstrate significant improvements in both routing and resolution with the use of multi-modal ticket analysis compared to only text based analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
