Protocol-Sensitive Evaluation of Log Anomaly Detection: Component Costs and Target-Access Sensitivity on HDFS and BGL
cs.DB, cs.AI, cs.CE, cs.IR, cs.LG
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: Accepted at DASC 2026. 8 pages, 1 figure, 8 tables. Reproduction support artifact: https://doi.org/10.5281/zenodo.23151002
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Efficient Estimation of Word Representations in Vector Space
- LogLLM: Log-based Anomaly Detection Using Large Language Models
Related papers
- Cost-Aware Mixture-of-Experts Coordination for Model Markets
- Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries
- Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering
- MetaJoin: Adaptive Join Selections for Heterogeneous Tables
- Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation
- DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation