标题： 面向操作数据智能聊天机器人——虚拟知识图谱就是你需要的一切 显示英文标题

标题： Towards Operational Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need

摘要： 随着生成式人工智能对计算科学计算的挑战，数据中心在规模和数据量方面正经历前所未有的增长。 因此，计算效率变得比以往任何时候都更加关键。 操作数据分析（ODA）依赖于数据中心遥测数据的收集来提高效率，但到目前为止，主要集中在实时遥测数据可视化和事后分析上。 然而，随着非关系型数据库现在作为默认存储后端以支持可扩展性，由于其无模式的特性，查询这些数据具有挑战性，这需要领域知识来遍历数据源之间的关系。 本体和知识图谱（KGs）可以捕捉这些关系，但传统的KGs扩展成本高昂，且尚未广泛应用于多变量时间序列。 虚拟知识图谱（VKGs）通过在运行时生成特定查询的图谱，提供了一种轻量级的替代方案。 在本工作中，我们提出了一种完整的端到端ODA聊天机器人系统，该系统使用大型语言模型（LLM）生成SPARQL查询，并利用VKG进行数据检索。 这种方法与直接的NoSQL查询相比，准确率达到了92.5%，而直接的NoSQL查询只有25%。 所提出的方法优化了VKG构建和LLM推理，将之前工作的平均查询延迟减少了85%（从20.36秒减少到3.03秒），并保持VKG大小在179 MiB以下。 这种性能使该工具适合部署和与ODA终端用户进行实时交互。 显示英文摘要

摘要： With generative artificial intelligence challenging computational scientific computing, data centers are experiencing unprecedented growth in both scale and volume. As a result, computing efficiency has become more critical than ever. Operational Data Analytics (ODA) relies on the collection of data center telemetry to improve efficiency, but so far has been focusing on real-time telemetry data visualization and post-mortem analysis. However, with NoSQL databases now serving as the default storage backend to support scalability, querying this data is challenging due to its schema-less nature, which requires domain knowledge to traverse relationships between data sources. Ontologies and Knowledge Graphs (KGs) can capture these relationships, but traditional KGs are costly to scale and have not been widely applied to multivariate timeseries. Virtual Knowledge Graphs (VKGs) offer a lightweight alternative by generating query-specific graphs at runtime. In this work, we present a full end-to-end ODA chatbot system that uses a Large Language Model (LLM) to generate SPARQL queries, utilizing VKG for data retrieval. This approach achieves 92.5% accuracy compared to 25% with direct NoSQL queries. The proposed methodology optimizes VKG construction and LLM inference, cutting previous work average query latency by 85% (from 20.36s to 3.03s) and keeping VKG sizes under 179 MiB. This performance makes the tool suitable for deployment and real-time interaction with ODA end-users.