Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction
Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business insights remains a fundamental challenge. Data engineers and analysts expend substantial effort reconciling semantic discrepancies, hand-crafting parsing logics, and maintaining fragile mappings between raw data and business KPIs. In this paper, we present an end-to-end framework that fully automates the construction of a business semantic layer from application raw logs. Our approach introduces a two-stage semantic abstraction: first, high-level business features are identified via LLM inference augmented with domain-specific industry knowl...
| Authors | Yuanzhe Jia, Ali Anaissi |
|---|---|
| Primary category | cs.CL |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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