Debugging Tabular Log as Dynamic Graphs
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908735347097600 |
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| author | Liang, Chumeng Jin, Zhanyang Akhtar, Zahaib Pereira, Mona Yu, Haofei You, Jiaxuan |
| author_facet | Liang, Chumeng Jin, Zhanyang Akhtar, Zahaib Pereira, Mona Yu, Haofei You, Jiaxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22903 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Debugging Tabular Log as Dynamic Graphs Liang, Chumeng Jin, Zhanyang Akhtar, Zahaib Pereira, Mona Yu, Haofei You, Jiaxuan Machine Learning Computation and Language 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. |
| title | Debugging Tabular Log as Dynamic Graphs |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2512.22903 |