  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.
