Tabular log abstracts objects and events in the real-world system and reports their updates to reflect the change of the system, where one can detect real-world inconsistencies efficiently by debugging corresponding log entries. However, recent advances in processing text-enriched tabular log data overly depend on large language models (LLMs) and other heavy-load models, thus suffering from limited flexibility and scalability. This paper proposes a new framework, GraphLogDebugger, to debug tabular log based on dynamic graphs. By constructing heterogeneous nodes for objects and events and connecting node-wise edges, the framework recovers the system behind the tabular log as an evolving dynamic graph. With the help of our dynamic graph modeling, a simple dynamic Graph Neural Network (GNN) is representative enough to outperform LLMs in debugging tabular log, which is validated by experimental results on real-world log datasets of computer systems and academic papers.

翻译：表格日志对现实世界系统中的对象和事件进行抽象，并报告其更新以反映系统的变化，从而可以通过调试相应的日志条目来高效地检测现实世界中的不一致性。然而，当前处理富含文本的表格日志数据的方法过度依赖大型语言模型（LLMs）及其他高负载模型，因而存在灵活性与可扩展性受限的问题。本文提出了一种新框架 GraphLogDebugger，用于基于动态图进行表格日志调试。该框架通过为对象和事件构建异构节点并连接节点间边，将表格日志背后的系统恢复为一个演化的动态图。借助我们的动态图建模，一个简单的动态图神经网络（GNN）即具有足够的代表性，在调试表格日志方面能够超越 LLMs，这一结论在计算机系统和学术论文的真实世界日志数据集上的实验结果中得到了验证。