Lightweight Multi-System Multivariate Interconnection and Divergence Discovery
This addresses fault discovery and diagnostics in complex systems like the CMS experiment at CERN, but it appears incremental as it builds on existing multivariate analysis and information retrieval techniques.
The study tackled the problem of identifying outlier behavior in large multi-system environments with multivariate data, presenting a lightweight mechanism (LIDD) that clusters readout systems and sensors consistent with expected configurations while capturing unusual behavior and estimating root causes.
Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in multi-system environments. The approach employs a multivariate analysis technique that first estimates the similarity heatmaps among the sensors for each system and then applies information retrieval algorithms to provide relevant multi-level interconnection and discrepancy details. Our experiment on the readout systems of the Hadron Calorimeter of the Compact Muon Solenoid (CMS) experiment at CERN demonstrates the effectiveness of the proposed method. Our approach clusters readout systems and their sensors consistent with the expected calorimeter interconnection configurations, while capturing unusual behavior in divergent clusters and estimating their root causes.