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Summary
This summary is machine-generated.

Trace-LogVector (TLV) enhances system log analysis by preserving execution order and entity relationships. This relational log representation significantly improves retrieval-augmented generation (RAG) for diagnosing complex cloud systems.

Keywords:
IoT cloud systemsTrace-LogVectorinformation retrievalrelational log representationretrieval-augmented generationsensor-driven systemssystem log analysis

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Engineering

Background:

  • System logs in IoT and cloud environments capture critical execution traces and service interdependencies.
  • Current retrieval-augmented generation (RAG) methods often treat logs as flat text, losing vital relational and sequential information.
  • Diagnosing complex service pipelines in sensor-driven cloud backends requires preserving execution flow and entity interactions.

Purpose of the Study:

  • To introduce Trace-LogVector (TLV), a novel relational log representation for system logs.
  • To evaluate TLV's effectiveness in preserving execution order and entity interactions for improved log analysis.
  • To compare the performance of TLV against traditional single-chunk log representations in retrieval tasks.

Main Methods:

  • Developed Trace-LogVector (TLV) based on the Chunk as Relational Data (CARD) principle, creating entity-centric, multi-chunk structures.
  • Utilized publicly reproducible backend execution logs for evaluating TLV.
  • Conducted controlled experiments comparing single-chunk and multi-chunk TLV representations under identical embedding and retrieval settings.
  • Quantitatively assessed retrieval performance using Hit@5 and Mean Reciprocal Rank at 5 (MRR@5).

Main Results:

  • The multi-chunk TLV representation achieved a Hit@5 of 1.000 and an MRR@5 of 0.900.
  • TLV consistently outperformed the single-chunk baseline across all evaluation queries.
  • Preserving execution contexts and entity relationships as relational retrieval units significantly improved RAG-based log analysis.

Conclusions:

  • Trace-LogVector (TLV) offers a superior method for representing system logs in IoT and cloud environments.
  • Relational log representation is crucial for enhancing the accuracy and efficiency of RAG in diagnosing complex systems.
  • TLV facilitates better monitoring and diagnosis of large-scale sensor networks and cloud systems.