Sentinel: Signal Validation and Anomaly Detection for Enterprise Log Data
Modern enterprise systems generate large volumes of operational logs that contain information about system behavior, performance, and failure conditions.These logs are frequently used for troubleshooting, monitoring, and anomaly detection.However, raw logs are typically unstructured and heterogeneous, making it difficult to determine whether they contain meaningful signals for analytical methods.Applying anomaly detection algorithms without first validating the quality and informational content of the data can lead to misleading results or unnecessary computational costs.
Authors
- Daniel Monsalve Muñoz (ORCID: https://orcid.org/0009-0003-4775-2058)
- J.M. Vergara (ORCID: https://orcid.org/0000-0002-5401-5342)
- Sebastián Osorio Agudelo (ORCID: https://orcid.org/0009-0004-9903-1807)
- Juan Pablo Aguilar Calle (ORCID: https://orcid.org/0009-0001-9109-393X)
- Nicolás Laverde Manotas (ORCID: https://orcid.org/0009-0002-3578-694X)
- Julián David Muñoz Pertuz (ORCID: https://orcid.org/0009-0006-0076-1696)
- Jeisson Vicente Niño Castillo (ORCID: https://orcid.org/0009-0009-2320-8246)
Publication Details
- Journal
- The Journal of Open Source Software
- Published
- 2026-11-07
- DOI
- https://doi.org/10.21105/joss.11275
- Primary Topic
- Software System Performance and Reliability
- Type
- article
- Field-Weighted Citation Impact
- 0.00