The technical support team of a supercomputing centre accumulates, over the course of decades, a large volume of resolved incidents that constitute critical operational knowledge. At the Galician Supercomputing Center (CESGA) this history has been managed for over twenty years with Request Tracker (RT), whose built-in search engine has significant limitations that hinder knowledge reuse by the support staff. This paper presents Fragata, a semantic ticket search system that combines modern information retrieval techniques with the full RT history. The system can find relevant past incidents regardless of language, the presence of typos, or the specific wording of the query. The architecture is deployed on CESGA's infrastructure, supports incremental updates without service interruption, and offloads the most expensive stages to the FinisTerrae III supercomputer. Preliminary results show a substantial qualitative improvement over RT's native search.

翻译：超级计算中心的技术支持团队在数十年间积累了海量已解决的故障事件，这些事件构成了关键运维知识。在加利西亚超级计算中心(CESGA)，该历史数据已通过请求追踪系统(RT)管理超过二十年，但其内置搜索引擎存在显著局限，阻碍了支持人员对知识的复用。本文提出Fragata语义工单检索系统，融合现代信息检索技术与完整RT历史数据。该系统可跨语言、容错拼写错误并适配查询语义，精准定位相关历史事件。该架构部署于CESGA基础设施，支持无服务中断的增量更新，并将最耗时阶段卸载至FinisTerrae III超级计算机。初步结果表明，该系统相比RT原生搜索实现了质的飞跃。